# Textual Data Mining for Financial Fraud Detection: A Deep Learning Approach

Qiuru Li  Department of Information Systems, HKUST

###### Abstract

In this report, I present a deep learning approach to conduct a natural
language processing (hereafter NLP) binary classification task for analyzing
financial-fraud texts. First, I searched for regulatory announcements and
enforcement bulletins from HKEX news to define fraudulent companies and to
extract their MD&A reports before I organized the sentences from the reports
with labels and reporting time. My methodology involved different kinds of
neural network models, including Multilayer Perceptrons with Embedding layers,
vanilla Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and
Gated Recurrent Unit (GRU) for the text classification task. By utilizing this
diverse set of models, I aim to perform a comprehensive comparison of their
accuracy in detecting financial fraud. My results bring significant
implications for financial fraud detection as this work contributes to the
growing body of research at the intersection of deep learning, NLP, and
finance, providing valuable insights for industry practitioners, regulators,
and researchers in the pursuit of more robust and effective fraud detection
methodologies.

††journal: Journal Name

##  1 Introduction

NLP, as a key node of Artificial Intelligence, has been developed as one of
the trendiest techniques in not only the finance and accounting literature for
financial texts analysis [1, 2], but also in the financial practice, for
instance, risk assessments, auditing and stock behavior predictions [3];
Healthcare and automotive industries are the leaders of NLP nowadays. However,
there are limitations for NLP algorithms given that they can neither
understand the entire context and analyze a text as a bag of individual words
nor learn the grammar and word order [4]. On such grounds, deep learning NLP
were evolved to diminish these insufficiencies, for example, word2vec [5], RNN
[6], RNN variants (LSTM [7], GRU [8], etc.), and Attention Mechanism [9] which
can conduct various text analysis tasks, including sentiment analysis,
sentence-level classification and image captioning. With deep learning NLP
algorithms, we can dig out more insights from the analysis of human languages.

RNN, with the recurring mechanism which can feed the results back to the
neural network, is powerful for analyzing sequential data, such as texts and
videos. Though RNN considers the previous and current information at the same
time, it has a short-term memory problem due to the vanishing gradient
problem. Therefore, RNN variants, GRU and LSTM were innovated to tackle these
deficiencies of RNN. LSTM and GRU are the most widely used RNN variants as
they can easily learn long-distance dependencies. Besides, there are
bidirectional RNNs and multi-layer RNNs which have higher computation
capability for more complicated representations further improving the
performance of RNNs. Therefore, in this paper, I am going to learn the
performance of different RNN algorithms through the task of financial fraud
detection, classifying fraudulent and non-fraudulent sentences, and comparing
the accuracy of various RNN models. With this research, regulatory authorities
and financial companies can gain more useful insights into developing
financial fraud detection techniques with the application of deep learning NLP
algorithms.

##  2 Methodology

###  2.1 Deep Learning NLP Algorithms

####  2.1.1 Recurrent Neural Networks (RNN)

RNN is a type of neural network where the output from the previous step is fed
as the input to the current step. RNN has the present and the recent past as
two inputs and applies weights to these inputs. The weights are adjusted for
both backpropagations through time and gradient descent. Within
backpropagation through time, the error is backpropagated from the last to the
first timestep, while unrolling all the timesteps. This allows calculating the
error for each timestep, which allows updating the weights. The gradient is
for updating the weights and exponentially shrinks up or down during the
backpropagation, which can lead to exploding gradient or vanishing gradient
that makes RNN difficult to retain information in the earlier layers.

With internal memory, RNN is able to handle sequential or time series data and
temporal problems, such as language translation and natural language
processing. RNN is used by Apple’s Siri and Google’s voice search.

####  2.1.2 Long-Short Term Memory (LSTM)

LSTM is an advanced RNN that can also learn the dependence of sequential data,
which mitigates the vanishing gradient descent issue by preserving the
information from earlier time steps. LSTM is powerful in speech recognition,
video analysis, and sentiment analysis. Moreover, LSTM can handle complicated
datasets accurately.

LSTM has three gates: forget gate decides what is kept and forgotten from the
previous cell state; The input gate controls what parts of the new cell
content are written to the cell; The output gate determines what parts of the
cell are output to hidden state. The picture below illustrates the operations
of LSTM.

Figure 1: LSTM’s gate structures.

####  2.1.3 Gated Recurrent Unit (GRU)

GRU is an advanced version of RNN that can also tackle sequential data and
resolve the problem of vanishing gradient descent. GRU is very similar to
LSTM, but with only two gates, which makes it faster in training and more
preferred to small datasets since GRU takes fewer parameters.

GRU has a reset gate, which is responsible for short-term memory and controls
what parts of the hidden state are updated or preserved, and an update gate
for long-term memory, which decides what parts of the previous hidden state
are used to compute new content separately. The picture below shows the
operations of GRU.

Figure 2: LSTM’s gate structures.

####  2.1.4 Bidirectional recurrent neural networks (BRNN)

BRNN connects two hidden layers of opposite directions to the same output.
BRNN can be trained without the limitation of using input information just up
to a present future frame. This is accomplished by training it simultaneously
in the positive and negative time directions [10], hence, the output layer can
get information from backward and forward states at the same time. BRNN is
powerful if there is access to the entire input sequence, and especially
useful when the context of the input is needed.

###  2.2 Data Collection and Organization

In order to conduct financial fraud detection, we have searched for fraudulent
cases reported by HKEX news from December 2000 to July 2022. Then we defined
the case period, fraudulent theme, relevant document, and reported time
through reading the news. According to this organized information, we
downloaded relevant documents, i.e., financial statement, annual report, and
MD&A, for the case period and extracted sentences from the documents. Finally,
we labeled the sentences and time of the case in order to conduct further
model training. The label includes “F” as fraudulent and “NF” as non-
fraudulent.

###  2.3 Data processing

We have 34838 samples with features “textual data”, “label” and “time” in
total. First, to make it more convenient to group and classify, I converted
the label into a numeric variable. “F” is represented by 1 and “NF” is
represented by 0. Second, I conducted data cleansing by removing stop words
for textual data columns and split the whole dataset into train and test sets.
Then, I further cleaned and organized the two datasets by separating textual
data and labels, removing symbols, and lowering the case for textual data.
From the collected data I observed that the number of “F” and “NF” labeled
sentences are very imbalanced, I used oversampling to increase the sample
number of the minority class. Finally, I applied a tokenizer to generate the
word index dictionary and list of token sequences from the training texts,
then pad the sequences to a uniform length for both training and test texts to
prepare for the model training and fitting.

###  2.4 Model building

Considering the training parameters, I use the binary cross entropy loss since
the financial-fraud text detection is a binary classification task; Adam as
the optimizer because it has faster convergence and can easily handle heavy-
tailed noise; AUC (Area Under Curve) as the evaluation metric since our
dataset is imbalanced, using AUC can avoid fake high accuracy since AUC
reflects true positive rate and false positive rate of prediction. For the
model fitting parameters, since the dataset is large and imbalance, I use a
large batch size and train for a small number of epochs to enhance the
predicting efficiency.

Model |  Hyperparameters |  Layers |  Training parameters
---|---|---|---
Simple NN with Embedding layers |  |  1 Embedding layer, 1 flatten layer and 2 dense layers |
Vanilla RNN |  |  1 Embedding layer, 1 bidirectional vanilla RNN layer and 2 dense layers |
LSTM |  vocab_size = 20000, embedding_dim = 150, input_length = maxlen = 200 |  1 Embedding layer, 1 Bidirectional LSTM layer, 2 dense layers, 2 dropout layers and 1 Batch Normalization layer |  Loss: binary cross entropy, Optimizer: Adam, Metrics: AUC, batch_size = 256, epoch = 10
GRU |  |  1 Embedding layer, 1 bidirectional GRU layer, 2 dense layers |
Multi-layer LSTM |  |  1 Embedding layer, 2 Bidirectional LSTM layers, 2 dense layers, 2 dropout layers and 1 Batch Normalization layer |
Table 1: An overview of model architecture and training configurations.

##  3 Results

To illustrate easier comparison among neural network models, I extracted the
performance data from the epoch with best validation accuracy for reference.

Model |  Training loss |  Training AUC |  Validation loss |  Validation AUC
---|---|---|---|---
Simple NN with embedding layers |  0.0350 |  0.9998 |  0.0612 |  0.5112
Vanilla RNN |  0.0006 |  1.0000 |  0.0564 |  0.5118
LSTM |  0.0014 |  1.0000 |  0.0545 |  0.5087
GRU |  0.0002 |  1.0000 |  0.0991 |  0.5111
Multi-layer LSTM |  0.0130 |  0.9999 |  0.6405 |  0.5990
Table 2: Training and validation statistics of different models.

##  4 Discussion

As the training loss and AUC describes how well the model performs on the
training dataset and the validation loss and AUC implies how well the model
fits the new data, the model should be regarded as well-performed if it has a
higher AUC for both the training and validation dataset. The results of the
best model regarding the validation AUC reveal that the multi-layer LSTM model
is slightly better than the other models, and the overall validation loss
shows that the LSTM is more well-performed since the LSTM model’s validation
loss are decreasing during the training and predicting while the others are
increasing. However, the overall performance shows that the validation AUC for
the final model is around 0.5 and the ultimate training AUC achieved 1 for all
five models, which reflects the overfitting problem and the low prediction
abilities of the five neural models. However, in principle, neural network
models, especially RNNs should perform well in tackling text classification
tasks. From my point of view, the reason of unsatisfactory prediction results
in this

financial-fraud text detection task can be the imbalance of our samples. The
number of samples with the label “NF” is 175 times of samples with the label
“F”, which can lead to bias towards the majority class only. Additionally, in
real life, a majority of companies won’t be fraudulent. Therefore, it will be
difficult for the models to predict the minority fraudulent class. Although
the application of deep learning NLP algorithms is powerful and practical in
the financial industry currently, it is challenging to conduct financial-fraud
text detection.

##  5 Acknowledgments

I would like to express my sincere gratitude to the organizers of the
Undergraduate Research Opportunity Program (UROP), which provides a platform
for students to conduct research. Moreover, I would like to show my
appreciation to Professor HUANG, and Professor Yang since they gave me great
support throughout the whole research project, and also my research teammates
as we collaborated together to tackle the obstacles.

## References

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  * Huang et al. [2023] A. H. Huang, H. Wang, Y. Yang,  Finbert: A large language model for extracting information from financial text,  Contemporary Accounting Research 40 (2023) 806–841.
  * Kang et al. [2020] Y. Kang, Z. Cai, C.-W. Tan, Q. Huang, H. Liu,  Natural language processing (nlp) in management research: A literature review,  Journal of Management Analytics 7 (2020) 139–172.
  * Loughran and McDonald [2016] T. Loughran, B. McDonald,  Textual analysis in accounting and finance: A survey,  Journal of Accounting Research 54 (2016) 1187–1230.
  * Mikolov et al. [2013] T. Mikolov, K. Chen, G. Corrado, J. Dean,  Efficient estimation of word representations in vector space,  arXiv preprint arXiv:1301.3781 (2013).
  * Rumelhart et al. [1985] D. E. Rumelhart, G. E. Hinton, R. J. Williams, et al., Learning internal representations by error propagation, 1985.
  * Hochreiter and Schmidhuber [1997] S. Hochreiter, J. Schmidhuber,  Long short-term memory,  Neural computation 9 (1997) 1735–1780.
  * Chung et al. [2014] J. Chung, C. Gulcehre, K. Cho, Y. Bengio,  Empirical evaluation of gated recurrent neural networks on sequence modeling,  arXiv preprint arXiv:1412.3555 (2014).
  * Vaswani et al. [2017] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin,  Attention is all you need,  Advances in neural information processing systems 30 (2017).
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11institutetext: Machine & Hybrid Intelligence Lab, Department of Radiology,
Northwestern University, Chicago, USA,
Department of ECE, The University of Texas at Austin, USA

# MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation

Debesh Jha  Nikhil Kumar Tomar  Koushik Biswas  Gorkem Durak  Matthew Antalek
Zheyuan Zhang  Bin Wang  Md Mostafijur Rahman  Hongyi Pan  Alpay
Medetalibeyoglu  Vandan Gorade  Abhijit Das  Yury Velichko  Daniela Ladner
Amir Borhani  Ulas Bagci

###### Abstract

Accurate segmentation of organs from abdominal CT scans is essential for
clinical applications such as diagnosis, treatment planning, and patient
monitoring. To handle challenges of heterogeneity in organ shapes, sizes, and
complex anatomical relationships, we propose a  Multi decoder network (MDNet),
an encoder-decoder network that uses the pre-trained MiT-B2 as the encoder and
multiple different decoder networks. Each decoder network is connected to a
different part of the encoder via a multi-scale feature enhancement dilated
block. With each decoder, we increase the depth of the network iteratively and
refine segmentation masks, enriching feature maps by integrating previous
decoders’ feature maps. To refine the feature map further, we also utilize the
predicted masks from the previous decoder to the current decoder to provide
spatial attention across foreground and background regions. MDNet effectively
refines the segmentation mask with a high dice similarity coefficient (DSC) of
0.9013 and 0.9169 on the Liver Tumor segmentation (LiTS) and MSD Spleen
datasets. Additionally, it reduces Hausdorff distance (HD) to 3.79 for the
LiTS dataset and 2.26 for the spleen segmentation dataset, underscoring the
precision of MDNet in capturing the complex contours. Moreover, MDNet is more
interpretable and robust compared to the other baseline models. The code for
our architecture is available at https://github.com/xxxxx/MDNet.

###### Keywords:

Liver segmentation spleen segmentation transformer computed tomography multi-
decoder network

##  1 Introduction

The liver and spleen are vital organs for maintaining metabolism, immunity,
and hematopoiesis [15]. The liver is mainly responsible for metabolic
activities, whereas the spleen controls blood filtration [18]. The size of the
liver and spleen varies among the different populations due to age, body size,
ethnicity, etc. [16]. Liver cancer is considered the third most common cause
of cancer-related death worldwide [2]. The spleen is one of the areas of
widespread involvement of lymphoma. Computed Tomography (CT) is critical in
diagnosing and characterizing liver and spleen lesions [7]. However, liver and
spleen segmentation are among the most challenging organs to segment because
of their highly variable shape, variations in morphology, location,
orientation, and intensity values [7]. Our clinical motivation in this work
comes from the fact that the liver and spleen are part of a network that also
includes the portal vein and its branches, which carry blood from the
gastrointestinal tract and spleen to the liver. Therefore, there is a desire
to measure liver and spleen volume, necessitating an efficient automated
segmentation approach.

There have been several studies on liver and spleen segmentation [9, 20, 10,
6]. Jha et al. [9] proposed PVTFormer architecture that used a pretrained
pyramid vision transformer as an encoder and combined it with advanced
residual upsampling and decoder block. They obtained a high DSC of 0.8678 and
mIoU of 0.7846 for healthy liver segmentation. Lee et al. [10] developed two
deep-learning models to measure liver segmental volume ratio (LSVR) and spleen
volumes from CT scans to predict cirrhosis and advanced fibrosis. In two
datasets, the best-performing multivariable model achieved AUCs of 0.94 and
0.79 for cirrhosis and 0.80 and 0.71 for advanced fibrosis. They showed that
the CT-based model performed similarly well to that of radiologists. Again,
Demir et al. [6] utilized a hybrid approach by combining the Transformer with
the Generative Adversarial Network (GAN) and obtained a high DSC of 0.9433.

While these architectures offer high segmentation accuracy, they might have
difficulty handling variable morphology and complex anatomical structures
across different individuals in the liver and spleen. Additionally, these
models are complex and challenging to interpret, which limits clinical trust.
This work addresses two fundamental research questions: (I) Can we develop a
fully automatic, reliable, and accurate CT liver and spleen segmentation
architecture? (II) Can we develop an interpretable model to visualize the
model’s decision-making process through the different decoder stages to
enhance trust and understanding? To address the above research questions, we
design a novel architecture, Multi decoder network (MDNet). The key
contribution of this work is as follows:

  1. 1.

Multi-decoder learning — MDNet explores the use of increasing depth in
different decoders to achieve a better understanding of the liver and spleen
morphology and shapes. This design allows for incremental refinement of
segmentation masks, leveraging the output of the preceding decoders as spatial
attention for the subsequent one, which increases the network’s ability for
better localization of organs.

  2. 2.

Interpretability through visualization — Our architecture is designed in a way
that both intermediate predicted masks and final predicted mask can be
visualized through different layers. The visualization of intermediate and
final masks shows the effect of different decoders, demonstrating and enabling
better interpretability of the MDNet (see – Figure 1 decoder outputs and
Figure 2 (heatmap)).

  3. 3.

Systematic evaluation — We evaluated MDNet on two publicly available liver and
spleen datasets, outperforming six SOTA segmentation methods.

##  2 Method

Figure 1: The block diagram shows an overview of the proposed MDNet. A MiT-B2
encoder processes the input image to extract feature maps at four different
levels (F1, F2, F3, and F4). Each decoder network is connected to a different
part of the decoder via a multi-scale feature enhancement dilated block to
increase the depth of the network to predict three distinct segmentation
masks. Additionally, the decoders are connected in a way that the output
feature from the preceding decoders is utilized in the subsequent one to
refine the segmentation output further. Moreover, we also use the predicted
masks of the prior decoder in the subsequent decoder for the further
refinement of the feature map. This process provides spatial attention across
foreground and background regions and enhances the final segmentation results.

Figure 1 shows the proposed MDNet architecture schematic diagram. We employ
MiT-B2 [19] as the pre-trained encoder, from which we extract four distinct
feature maps. The core component of MDNet lies in its three individual
decoders, each increasing in complexity, thus progressively enhancing the
depth and representational capability of the network. Additionally, MDNet
integrates Multi-Scale Feature Enhancement Dilated Blocks, Mask attention
blocks, and decoder blocks to capture a richer feature representation and
improve organ delineation.

###  2.1 Mix Transformer (MiT) Encoder

Ideally, we should choose a network with high performance and balanced
computational efficiency for accurate abdominal organ segmentation. Therefore,
in the MDNet, we utilize MiT-B2, a variant of mix transformer, as a pretrained
encoder that was previously trained in the ImageNet-1k dataset. An input image
$I\in\mathbb{R}^{H\times W\times 3}$ is fed to the MiT-encoder to obtain four
distinct multi-scale feature maps
$F_{i}\in\mathbb{R}^{\frac{H}{2^{i+1}}\times\frac{W}{2^{i+1}}\times C_{i}}$,
where $i\in\\{1,2,3,4\\}$ and $C_{i}\in\\{64,128,320,512\\}$. These feature
maps are important in capturing intricate details and varying contexts from
the input abdominal images.

###  2.2 Dilated Convolution (DC) Block

We have utilized the dilated convolution (DC) block in our architecture to
enhance the feature representation of the different multi-scale feature maps
from the MiT-B2 encoder. The block begins with four parallel $3\times 3$
convolutional layers having a dilation rate of {$1,3,6,9$}, followed by a
batch normalization layer and a ReLU activation function. These dilation rates
help determine the spacing between the elements in the convolutional filters,
effectively enlarging the receptive field. Subsequently, the outputs from
these dilated convolutional layers are concatenated to combine the multi-scale
information learned at different dilation rates. This concatenated feature map
is then passed through a $1\times 1$ convolutional layer to integrate and
refine the information from the multiple scales. Finally, the output from this
convolutional operation is further refined using the Channel Attention and
Spatial Attention mechanisms of the Convolutional Block Attention Module
(CBAM) [17], enhancing the network’s ability to focus on relevant features
while suppressing irrelevant ones, thus improving segmentation outcomes.

###  2.3 Multi-Scale Feature Enhancement Dilated (MSFED) Block

The MSFED block is a pivotal component designed to enhance feature
representation by incorporating multi-scale information extracted from the
MiT-encoder, which is further passed through the dilated convolution to
further refine the feature representation process. With four encoder features
available, the first feature directly undergoes processing through the dilated
convolution block. Subsequently, the remaining three encoder feature maps are
channeled into three distinct MSFED blocks, each responsible for handling a
specific scale. Each MSFED block operates by taking the current scale encoder
feature along with the previously processed scale features as inputs.

The MSFED block for the second encoder feature map, denoted as $F_{2}$, is
structured as follows. Initially, the feature map $F_{1}$ undergoes a series
of $3\times 3$ Conv-BN-ReLU layers, followed by a $2\times 2$ max-pooling
operation to reduce its spatial dimension. The resulting output from the max-
pooling operation is then concatenated with the feature map $F_{2}$, creating
a combined feature representation. Subsequently, this combined representation
traverses through a series of $3\times 3$ Conv-BN-ReLU layers. This sequential
processing culminates in generating a unified feature representation,
traversing through the dilated convolution block for further refinement.
Following this initial MSFED block for the second encoder feature map, the
subsequent MSFED blocks are executed in a similar manner but with more layers
to accommodate more feature maps from the MiT encoder.

###  2.4 Mask Attention (MA) Block

In our MDNet architecture, the decoder component is equipped with Mask
Attention (MA) blocks to facilitate spatial attention across both foreground
and background regions within the feature maps. This block operates by
receiving the feature maps along with the mask predicted by the preceding
decoder. Initially, the input mask undergoes a resizing process followed by a
sigmoid activation function, resulting in pixel values confined within the
range of [0, 1]. Subsequently, two distinct masks are generated: the
foreground mask, denoted as $M_{f}$, and the complementary background mask,
represented as $M_{b}=1-M_{f}$. An element-wise multiplication is then
performed between both the foreground and background masks and the input
feature map, followed by sequential processing through a series of $3\times 3$
Conv-BN-ReLU layers. Furthermore, an element-wise addition operation is
executed between the feature map and the foreground mask $M_{f}$. The
resulting attenuated feature maps are concatenated {$\cup$} and subsequently
processed through additional $3\times 3$ Conv-BN-ReLU layers, followed by a
residual identity mapping connecting both input and output of the MA block.

$F_{out}=ReLU(C_{3\times 3}((C_{3\times 3}(x\odot M_{f})+M_{f})\cup(C_{3\times
3}(x\odot M_{b})+M_{f}))+C_{3\times 3}(x))$ (1)

###  2.5 Decoder Block

The decoder block begins with a $2\times 2$ transpose convolution layer,
doubling the spatial dimensions of the feature map, which is then concatenated
with the skip connection. This skip connection serves two crucial purposes:
first, it aids in preserving features that may be lost in deeper layers of the
network, and second, it facilitates gradient propagation during
backpropagation. Following this, two residual blocks are employed to
effectively learn semantic features essential for generating a segmentation
mask.

###  2.6 Multi-Decoder

The decoder utilizes the feature maps from the MiT-encoder and is then
processed by the MSFED block to generate the segmentation mask. All the three
decoders utilize the feature maps {$F_{1}$, $F_{2}$}, {$F_{1}$, $F_{2}$,
$F_{3}$} and {$F_{1}$, $F_{2}$, $F_{3}$, $F_{4}$} respectively. The initial
decoder comprises a single decoder block, starting with $F_{2}$ as input
feature maps and $F_{1}$ as the skip connection. Following this, the output
undergoes bilinear upsampling with a factor of four, succeeded by $1\times 1$
convolution and sigmoid activation to generate segmentation mask $M_{1}$.

Subsequent decoders maintain a parallel structure and integrate the Mask
Attention (MA) block to enhance feature representation and segmentation
performance. Additionally, feature maps from previous decoders at the same
scale are concatenated with a skip connection, which aids in feature
propagation, thereby enhancing performance. The second decoder comprises two
decoder blocks, each followed by a Mask Attention block. The resulting output
from the second decoder is used to yield a segmentation mask $M_{2}$.
Similarly, the third decoder comprises three decoder blocks, each
incorporating a Mask Attention block. The output from the third decoder
generates the final segmentation mask $M_{3}$.

##  3 Experiments and Results

###  3.1 Datasets:

We have utilized the Liver Tumor Segmentation Benchmark (LiTS) [3] benchmark
dataset for liver segmentation. This dataset comprises $131$ CT scans for
training and $70$ for testing, with only the training dataset publicly
available. To avoid bias, we split the training dataset into independent
training (91 scans), validation (20 scans), and test (20 scans) sets. During
preprocessing, we extracted the liver masks containing healthy liver and
tumors. Similarly, the Spleen segmentation dataset comes from the Medical
Segmentation Decathlon [1] study. This dataset includes $41$ CT scans for
training and $20$ for testing. Of these $41$ training scans, the first five
training scans were designated for testing, the following five for validation,
and the remaining $31$ for training. We resized the images to $512\times 512$
pixels in-plane resolution to balance training time and model complexity. The
volumetric CT scans were processed slice-by-slice to ensure compatibility with
our computational resources.

###  3.2 Implementation details and evaluation metrics

We use the PyTorch [12] framework for implementing and running all the
experiments. The experiments were performed on NVIDIA RTX A6000 GPU. The
network is configured to train with a batch size 16 and a learning rate set to
1e-4. We train all the models for 500 epochs to fine-tune the network
parameters adequately with an early stopping patience of 50. To enhance the
performance of our network, we use a combination of binary cross-entropy and
dice loss and an Adam optimizer for parameter updates. Liver and spleen
segmentation performance is evaluated using overlap-based metrics (dice
coefficient (DSC), mean intersection over union (mIoU), recall, precision, and
F2 score) and distance-based metrics such as Hausdorff distance (HD95).

###  3.3 Results

Results on LiTs datasets: For the baseline comparison, we compare MDNet with
CNN-based architectures (U-Net [14], Attention UNet [11], DeepLabv3+ [5]) and
recent transformer-based architecture such as TransUNet [4], HiFormer [8] and
G-Cascade [13]. From Table 3, it is evident that MDNet surpasses all other
State-of-the-art (SOTA) methods, with the highest DSC of 0.9383, mIoU of
0.9013, recall of 0.9427, and lowest HD value of 3.79. Similarly, MDNet
achieves superior scores than the recent transformer-based approaches in mDSC,
mIoU, recall, and HD, indicating fewer false positives.

Results on MSD Spleen datasets: Table 2 shows the results on the MSD spleen
dataset. From the experimental results, it can be observed that MDNet again
outperformed all SOTA methods with the highest Dice Coefficient (DSC) value of
0.9507, mIoU of 0.9176, recall of 0.9607, F2 value of 0.9558 and HD value of
2.26. HiFormer and G-Cascade again remained the most competitive networks,
with a DSC of 0.9358 and 0.9472, respectively. However, MDNet consistently
outperformed them in terms of DSC, mIoU, recall, and F2 in both datasets.
GCascade obtained a slightly better HD value of 0.01, which is negligible.
From the overall comparison, MDNet is the optimum choice.

Figure 2 shows the qualitative results. From the qualitative results, it can
be seen that the segmentation output produced by MDNet closely matches the
ground truth consistently as compared to UNet [14], HiFormer [8], and
G-Cascade [13] for both liver and spleen segmentation datasets. The
segmentation output produced by HiFormer and G-Cascade show over-segmentation
for some cases and under-segmentation for others. Precision is important in
clinical applications, and MDNet is the best choice. Table 3 shows the effect
of MDNet on LiTS benchmark dataset. From the Table, it can be observed that
there is an improvement in the performance as the network depth increases.

Table 1: Comparative evaluation of different models on the LiTS dataset [3]. Method | Publication | mDSC | mIoU | Recall | Precision | F2 | HD
---|---|---|---|---|---|---|---
U-Net [14] | MICCAI 2015 | 0.8719 | 0.8153 | 0.9082 | 0.8724 | 0.8874 | 4.32
Attention UNet [11] | MIDL 2018 | 0.8975 | 0.8442 | 0.9015 | 0.9291 | 0.8975 | 4.42
DeepLabv3+ [5] | ECCV 2018 | 0.9295 | 0.8895 | 0.9214 | 0.9610 | 0.9233 | 3.91
TransUNet [4] | Arxiv 2021 | 0.8671 | 0.8057 | 0.8789 | 0.8949 | 0.8721 | 4.69
HiFormer [8] | WACV 2023 | 0.9306 | 0.8924 | 0.9371 | 0.9460 | 0.9323 | 3.94
G-Cascade [13] | WACV 2024 | 0.9301 | 0.8921 | 0.9287 | 0.9549 | 0.9271 | 3.88
MDNet (Ours) | - | 0.9383 | 0.9013 | 0.9427 | 0.9514 | 0.9380 | 3.79
Table 2: Comparative results of different models on the MSD spleen dataset [1]. Method | Publication | mDSC | mIoU | Recall | Precision | F2 | HD
---|---|---|---|---|---|---|---
UNet [14] | MICCAI 2015 | 0.9126 | 0.8736 | 0.9226 | 0.9352 | 0.9161 | 2.52
Attention UNet [11] | MIDL 2018 | 0.8605 | 0.8009 | 0.8726 | 0.8869 | 0.8644 | 2.90
DeepLabv3+ [5] | ECCV 2018 | 0.9177 | 0.8753 | 0.9498 | 0.9217 | 0.9359 | 2.41
TransUNet [4] | Arxiv 2021 | 0.8402 | 0.7626 | 0.8669 | 0.8572 | 0.8482 | 3.48
HiFormer-L [8] | WACV 2023 | 0.9358 | 0.8959 | 0.9429 | 0.9417 | 0.9391 | 2.46
G-Cascade [13] | WACV 2024 | 0.9472 | 0.9135 | 0.9527 | 0.9573 | 0.9495 | 2.25
MDNet (Ours) | - | 0.9507 | 0.9176 | 0.9607 | 0.9536 | 0.9558 | 2.26

For interpretablity, we also present the heatmap in Figure 2. The heatmap on
the example liver and spleen images shows the MDNet’s precision in accurately
delineating the liver and spleen boundaries. MDNet utilizes 72.33 million
parameters, consumes 116.64 GMac flops, and can process at 39.70 frames per
second. A detailed comparative analysis of MDNet can be found in Supp. Table
1. More visualization of MDNet output can be found in the Suppl. material .Gif
file. MDNet’s high performance is due to its architectural design, which
integrates an iterative refinement mechanism at each decoder block, providing
continuous improvement. Incorporating multi-scale feature enhancement allows
MDNet to handle organ size and shape variations. The spatial attention through
mask attention blocks provides constant improvement and enables the model to
learn detailed and context-aware segmentation important for boundary
delineation.

Table 3: Effect of different decoders of MDNet on the LiTS dataset [3]. Method | mDSC | mIoU | Recall | Precision | F2 | HD
---|---|---|---|---|---|---
Decoder 1 | 0.9310 | 0.8922 | 0.9348 | 0.9467 | 0.9309 | 3.86
Decoder 2 | 0.9371 | 0.8991 | 0.9396 | 0.9515 | 0.9357 | 3.80
Decoder 3 | 0.9383 | 0.9013 | 0.9427 | 0.9514 | 0.9380 | 3.79
Figure 2: Qualitative comparison of models on LiTS [3] and MSD Spleen data
[1].

##  4 Conclusion

We proposed a novel method for abdominal organ segmentation, MDNet.
Integrating a pre-trained encoder, MiT-B2, dilated convolution block, multi-
scale feature enhancement dilated block, masked attention block, and decoder
block, MDNet provides a robust segmentation output. Qualitative results on the
two publicly available Liver CT and Spleen datasets showed that MDNet
surpasses all other established baselines for liver and spleen segmentation
tasks. The qualitative results and heatmap obtained by MDNet also highlighted
its clinical importance. In the future, we plan to test our algorithm on MRI
liver and spleen datasets built at our center. Also, volumetric computation of
medical scans with MDNet and other Transformer models will be explored in the
extended study.

## References

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  *[MDNet]: Multi decoder network
  *[SOTA]: State-of-the-art
  *[DSC]: Dice Coefficient


# FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-
Stage Tabular Deep Learning Network††thanks: This work was partially supported
by the Natural Sciences and Engineering Research Council (NSERC) of Canada
through the NSERC Discovery Grant RGPIN-2023-05654.

Sadaf Khademi  Concordia Ins. Inf. Syst. Eng. (CIISE)
Concordia University
Montreal, Canada
<EMAIL_ADDRESS>Zohreh Hajiakhondi  Dept. of Elec. and Comp. Eng.
(ECE)
Concordia University
Montreal, Canada
<EMAIL_ADDRESS>Golnaz Vaseghi  Applied Physiology
Research Center
Isfahan University of Medical Sciences
Isfahan, Iran
<EMAIL_ADDRESS>Nizal Sarrafzadegan  Isfahan Cardiovascular Research
Center
Isfahan University of Medical Sciences
Isfahan, Iran
<EMAIL_ADDRESS>Arash Mohammadi  Concordia Ins. Inf. Syst. Eng.
(CIISE)
Concordia University
Montreal, Canada
<EMAIL_ADDRESS>
###### Abstract

Familial Hypercholesterolemia (FH) is a genetic disorder characterized by
elevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated
genes. Early-stage and accurate categorization of FH is of significance
allowing for timely interventions to mitigate the risk of life-threatening
conditions. Conventional diagnosis approach, however, is complex, costly, and
a challenging interpretation task even for experienced clinicians resulting in
high underdiagnosis rates. Although there has been a recent surge of interest
in using Machine Learning (ML) models for early FH detection, existing
solutions only consider a binary classification task solely using classical ML
models. Despite its significance, application of Deep Learning (DL) for FH
detection is in its infancy, possibly, due to categorical nature of the
underlying clinical data. The paper addresses this gap by introducing the FH-
TabNet, which is a multi-stage tabular DL network for multi-class (Definite,
Probable, Possible, and Unlikely) FH detection. The FH-TabNet initially
involves applying a deep tabular data learning architecture (TabNet) for
primary categorization into healthy (Possible/Unlikely) and patient
(Probable/Definite) classes. Subsequently, independent TabNet classifiers are
applied to each subgroup, enabling refined classification. The model’s
performance is evaluated through $5$-fold cross-validation illustrating
superior performance in categorizing FH patients, particularly in the
challenging low-prevalence subcategories.

Index Terms—Cardiovascular Disease, Familial Hypercholesterolemia, Genetic
Disorder, TabNet, Tabular Data.

##  I Introduction

Familial Hypercholesterolemia (FH) is one of the most prevalent genetic
disorders characterized by abnormally high levels of blood cholesterol [1].
Its prevalence is estimated to range from $1$ in $200$ to $1$ in $300$
individuals across various ethnicities [2]. The history of FH traces its roots
to the pioneering work of the Norwegian physician, Dr. Carl Müller [3], who
shed light on the association between hypercholesterolemia and tendinous
xanthomas, connecting them to cardiovascular disease through the lens of
single-gene inheritance [4]. FH disorder is commonly caused by mutations in
genes responsible for regulating cholesterol metabolism, such as the Low-
Density Lipoprotein (LDL) receptor gene that can be passed down through
generations in families. This genetic disorder increases the risk of early-
onset cardiovascular diseases, including heart attacks and strokes, due to the
excessive buildup of LDL cholesterol in the arteries [6, 5]. Early detection
of FH is, therefore, not only cost-effective but also crucial for preserving
lives. However, only $10$% of the estimated number of worldwide affected
individuals have received a formal FH diagnosis. Out of this population, only
$2$% were identified before the age of $18$ years. For the majority of those
affected, FH goes unnoticed until middle ages, typically, surfacing around the
age of $45$ in tandem with the development of cardiovascular disease, which
highlights the urgent need for screening/diagnosis techniques at younger ages
[7, 8, 9, 10]. Indeed, without early recognition, many patients receive
inadequate treatment and miss valuable opportunities for preventing
cardiovascular problems, which can not only impact their quality of life but
may also shorten their lifespan. Despite extensive efforts in the medical
community, critical challenges (outlined later in Section II) persist for
early detection and timely intervention of FH. Leveraging the power of
Electronic Medical Records (EMRs) and Artificial Intelligence (AI), we aim to
address this gap. In this context, the paper proposes the FH-TabNet framework
that provides highly accurate early detection results without relying on
genetic data.

Contributions: The paper introduces an innovative framework for diagnosing FH
disorder in four distinct stages of progression, referred to as the Multi-
Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep
Learning Network (FH-TabNet). The proposed FH-TabNet framework is designed to
stage individuals with FH into the following four categories: Definite,
Probable, Possible, and Unlikely. A major challenge in this context is the low
prevalence of certain sub-categories, which renders the use of a single-stage
staging model infeasible. To address this challenge, the FH-TabNet adopts a
multi-stage approach, utilizing binary classification techniques built at
different stages based on a tabular learning architecture known as TabNet
[11]. In the first stage, FH-TabNet differentiates between combined Definite &
Probable category and combined Possible & Unlikely category. In the second
stage, two parallel binary classification models are designed to provide a
more refined within-category assessment of the FH risk stage. Sequential
attention is utilized for adaptive feature selection at each decision step,
which in turn enables the underlying TabNet to more efficiently conduct end-
to-end learning. In summary, the paper makes the following key contributions:

  * •

Introducing the intuitively pleasing FH-TabNet architecture, developed based
on tabular Deep Neural Networks (DNNs). The FH-TabNet, to the best of our
knowledge, is the first DL-based solution for multi-class FH risk
categorization, providing accurate predictions for low-prevalence
subcategories.

  * •

Providing accurate FH staging results without relying on genomic data.
Incorporation of EMRs and blood markers instead of genomic data sets FH-TabNet
apart from its counterparts by making it more cost-effective/accessible in
healthcare settings with limited resources.

Simulation results demonstrate a significant improvement in the reliability of
FH risk prediction when comparing FH-TabNet with traditional ML models. The
FH-TabNet achieved notably higher F1-scores, particularly in predicting the
low-prevalence subcategory of FH patients. More specifically, through $5$-fold
Cross-Validation (CV), it achieved average F1-scores of $79.20$% for Definite,
$87.20$% for Probable, $98.60$% for Possible, and $98.20$% for Unlikely FH
patients.

The rest of the paper is organized as follows: Section II, first, provides an
overview of the relevant literature within this field. Afterwards, Section
III, presents the data pre-processing phase and introduces TabNet as the
foundational component of the FH-TabNet framework. Section IV introduces the
FH-TabNet architecture. Simulation results are presented in Section V.
Finally, Section VI concludes the paper.

##  II Related Works

As stated previously, the term “hypercholesterolemia” was first introduced in
late $1930^{\prime}s$ by Carl Müller [3] conducting a study including $17$
families in which $68$ of $76$ members showed signs of heart disease. He
defined hypercholesterolemia patients with tuberous xanthomas and angina signs
and concluded that this disorder is hereditary with an autosomal (a specific
gene on a numbered chromosome rather than a sex chromosome) dominant
characteristic. When it comes to FH detection, Dutch Lipid Network Criteria
(DLNC), Simon Broome Registrar Criteria, and Make Early Diagnosis and Prevent
Early Death (MED-PED) criteria [12] represent conventional FH screening
methods widely employed in clinical settings for diagnosing FH. However, these
established models exhibit several drawbacks affecting their practical
application. The DLNC and Simon Broome criteria, incorporate lipid levels,
physical examinations, family history, and when accessible, genetic data. In
contrast, MED-PED criteria prioritize lipid levels and family history. The
subjective nature of family history assessments, coupled with scoring
variations and diagnostic threshold complexities, may lead to high levels of
inconsistency. Moreover, these conventional models also face challenges
related to resource accessibility, cost management, and potential population
variability, highlighting the need for more effective and accessible
diagnostic approaches.

Consequently, there has been a recent surge of interest in applying Machine
Learning (ML) techniques for the detection of FH. ML models have gained
considerable attention in the field of medical analysis and disease detection.
However, almost all of the recent research works [13, 16, 14, 15, 17], mainly
focused on binary classification of FH based on classical and hand-crafted ML
solutions. In other words, Deep Learning (DL) through the implementation of
DNNs has not fully infiltrated this domain, while, traditional ML techniques
have been featured in prestigious publications (i.e., Lancet) [18]. For
example, Reference [13] developed the FIND-FH model comprising two sequential
layers of Random Forest (RF) models, the first one of which is used for
feature selection. Incorporation of two consecutive RF layers enhanced model’s
performance and adaptability. Reference [16] evaluated the effectiveness of
various conventional ML techniques in improving the detection of FH and
assessed their clinical applicability within a substantial primary care
patient population. Similarly, in [14], a Logistic Regression (LR) model
employing the Least Absolute Shrinkage and Selection Operator (LASSO)
technique was utilized to discern predictive factors that effectively
distinguished individuals with FH. Likewise, Reference [15] devised a
classifier using Electronic Health Record (EHR) data from Stanford Health Care
to identify potential FH patients. The classifier, constructed as an RF model,
underwent training on data from confirmed patients and carefully matched non-
cases. Most other recent works such as [17], followed a similar approach and
focused on application of different classical ML models (i.e., RFs; Gradient
Boosting; Support Vector Machine (SVM), and; LR) for the task of FH detection.
Finally, Reference [19] focused on long-term risk prediction of FH by applying
supervised ML models aimed at developing highly efficient risk prediction
tools for FH occurrence. To identify the most effective solution, different
conventional ML models such as Naive Bayes, SVM, Decision Tree, and Ensemble
Learning were tested following a comprehensive hand-crafted feature analysis
step.

Figure 1: FH-TabNet architecture with its building blocks. BN, Agg, and FC
represent Batch Normalization, Aggregation, and Fully Connected, respectively.

In conclusion, while application of ML has been targeted for FH detection, to
the best of our knowledge, the focus of recent research works [13, 16, 14, 15,
17, 19] was restricted to development of conventional ML models considering a
binary classification problem. Application of DL models within this context is
in its infancy, possibly, due to the small size of available datasets and the
categorical nature of the underlying clinical data. The paper aims to address
this gap by targeting design of a domain-specific DL multi-class model.

##  III Materials and Methods

In this section, first, we will provide a concise overview of the tabular
dataset employed for developing and validating the proposed FH-TabNet
framework. Following that, we will present the pre-processing stage, which is
aimed to purify the dataset and make it appropriate for subsequent phases.

###  III-A Dataset

This study is developed based on an in-house dataset consisting of a cohort of
$1,929$ individuals with $390$ attributes after excluding patient
identification details. The primary classification of subjects within this
dataset relies on the FH Dutch score also known as the DLNC [20], which was
introduced as a scoring system for diagnosing FH in late $1990$s. The FH Dutch
score is a reliable cumulative metric considering a range of genetic,
clinical, and familial factors for determining an individual’s likelihood of
having FH. Based on this, individuals could be assigned to one of four
distinct hypercholesterolemia classes:

  * •

Definite FH: Individuals in this category, typically, have high FH Dutch
scores, approaching values higher than $8$.

  * •

Probable FH: FH Dutch scores between $5$ to $8$ reflect individuals with the
likelihood of having FH, but they may not meet all the criteria for a definite
diagnosis.

  * •

Possible FH: This group comprises individuals with FH Dutch scores falling in
the range of $3$ to $5$ indicating the potential presence of FH.

  * •

Unlikely FH: Individuals in this category demonstrate FH Dutch scores
approaching values lower than $3$ that make FH appear unlikely.

###  III-B Data Pre-processing: Refinement and Cleaning

Due to the significant proportion of missing values in the dataset, we adopt
the strategy of using feature columns with missing rates below $5$% and then
excluding individuals who have missing values. The aforementioned processing
steps result in creation of a more refined dataset, comprising of $1,591$
subjects and $50$ attributes. The contribution of samples within each class is
as follows, Definite FH $2.9$%, Probable FH $6.4$%, Possible FH $40.2$%, and
Unlikely FH $50.5$%. As can be derived from contribution values, if we merge
Definite and Probable FH cases in one group and Possible and Likely FH cases
in another it appears that the prevalence of individuals with FH is
approximately $1$ in $10$ within the dataset.

###  III-C Pre-processing: Categorization of Features

The final feature set includes a range of categorical and continuous
variables, which may encompass medical records of the patient, visible
evidence such as Xanthelasma and Corneal Arcus, blood-related markers such as
Fasting Blood Glucose (FBG), Cholesterol levels (total, High-Density
Lipoprotein (HDL), and Low-Density Lipoprotein (LDL)), and Triglycerides in
addition to clinical (myocardial infarction and cardiovascular disease), and
demographic (gender, age and weight) attributes. Categorical variables were
processed through a one-hot encoding technique. This method involves
converting categorical variables into binary vectors, where each category is
represented by a binary column.

##  IV The Proposed FH-TabNet Framework

As stated previously, accurate identification of FH patients is essential for
personalized treatment, risk assessment, genetic counseling, and resource
allocation. To accurately detect FH, the proposed FH-TabNet framework
classifies individuals into four categories, i.e., Definite, Probable,
Possible, and Unlikely FH. To this end, FH-TabNet is constructed as a multi-
stage and multi-step classification framework, sharing design strategies in
common with the Tabular Attention Network (TabNet) [11] architecture. Next, we
present the overall structure and building blocks of the FH-TabNet framework.

###  IV-A Overall Structure of FH-TabNet

As shown in Fig. 1, the FH-TabNet framework consists of three learning paths,
each a multi-step encoder architecture, configured in two stages. Stage-$1$ of
the FH-TabNet framework consists of a single $5$-step encoder path
distinguishing between healthy (Possible and Unlikely) and FH (Definite and
Probable) patients. In Stage-$2$, which is in series with Stage-$1$ utilizing
its outputs, there are two parallel $2$-step encoder paths to further refine
the classification into Definite/Probable and Possible/Unlikely individuals.
At each step, from the entire pool of available features in the training
dataset, a subset of features is dynamically selected to make predictions.
Such a feature selection approach is performed independently for each sample,
in contrary to some DNNs that apply a fixed selection across the entire
training dataset. Consequently, predictions for each sample are generated
using distinct feature subsets, resulting in improved performance.

The number of decision steps ($n\textsubscript{step}$) plays a critical role
in the model’s ability to learn data representations. Hence, the FH-TabNet
starts with a broad overview of the data and gradually refines the
representation to capture more intricate patterns. By using more decision
steps in Stage-$1$, we allow the model to capture a broader range of
relationships in the data, creating a foundational hierarchical
representation. Then, in Stage-$2$, with fewer decision steps but a higher
value of $n_{a}$ and $n_{d}$, we focus on refining the representation and
making more precise distinctions between classes. Decreasing the number of
decision steps in Stage-$2$ also helps to prevent overfitting due to the
smaller amount of data compared to the first stage.

###  IV-B Learning Pipeline of FH-TabNet

At first, the data undergoes a Batch Normalization (BN) layer to replace the
scaling process of the structured data. During each step, data sequentially
passes through Feature Transformer and Attentive Transformer blocks:

  * •

Feature Transformer Block (FTB), is responsible for generating an internal
feature representation. In each step, the FTB employs weights that are common
across all steps, alongside weights that are learned specifically for that
particular step. This approach ensures that each step’s internal
representation benefits from information derived from all steps. The internal
representation of features is then mapped into $n_{a}$ and $n_{d}$ components
through a split block. The mapped internal representation is then provided as
the input to the ATB and the decision layer (ReLU activation function),
respectively.

  * •

The Attentive Transformer Block (ATB), is responsible for feature selection at
each step. The ATB takes as input $n_{a}$ learned feature representations
(obtained from the split block of the FTB) and generates a mask. This mask,
which can be considered as a set of probabilities, is then used in the Mask
Block to decide which features should be utilized for the current step. In
other words, the feature masking layer uses the generated mask to select a
subset of features by conducting an element-wise product between the original
features and the generated mask.

Output of each step is computed by applying the ReLU activation on the $n_{d}$
dimensioned vector obtained from the split block. Finally, outputs of all the
decision steps are summed together and passed through a Fully Connected (FC)
layer to classify the FH risk stage.

##  V Simulation Results

TABLE I: 5-fold CV F1-score of the constituent parts of the proposed FH-TabNet framework. Stage | Class | Fold 1 | Fold 2 | Fold 3 | Fold 4 | Fold 5
---|---|---|---|---|---|---
1 | Patient | $0.91$ | $0.93$ | $0.93$ | $0.98$ | $0.92$
Healthy | $0.99$ | $0.99$ | $0.99$ | $1$ | $0.99$
2(P) | Probable | $0.92$ | $0.81$ | $0.87$ | $0.83$ | $0.93$
Definite | $0.84$ | $0.77$ | $0.85$ | $0.70$ | $0.80$
2(H) | Unlikely | $0.99$ | $0.98$ | $0.98$ | $0.99$ | $0.97$
Possible | $0.99$ | $0.99$ | $0.98$ | $0.99$ | $0.98$
TABLE II: Comparison (F1-score: mean $\pm$ standard deviation) with traditional ML models based on the 5-fold CV.  Model | F1-score
---|---
Unlikely FH | Possible FH | Probable FH | Definite FH
RF | $97.44\pm 0.61$ | $96.14\pm 0.82$ | $72.86\pm 8.09$ | $34.66\pm 9.62$
LDA | $93.94\pm 0.84$ | $93.70\pm 1.43$ | $72.38\pm 6.32$ | $65.54\pm 12.28$
LR | $93.12\pm 2.41$ | $90.20\pm 1.38$ | $33.44\pm 9.34$ | $19.64\pm 18.99$
Ridge | $86.52\pm 2.76$ | $83.96\pm 1.55$ | $0$ | $58.32\pm 10.74$
ADA | $86.36\pm 10.28$ | $77.82\pm 14.63$ | $17.30\pm 12.96$ | $20.00\pm 21.72$
KNN | $60.98\pm 5.45$ | $65.92\pm 4.61$ | $34.60\pm 27.85$ | $17.94\pm 17.49$
Single-Stage Multi-Class TabNet | $94.20\pm 1.93$ | $95.60\pm 2.15$ | $64.40\pm 7.68$ | $56.20\pm 12.28$
Proposed FH-TabNet | $\bm{98.20\pm 0.74}$ | $\bm{98.60\pm 0.48}$ | $\bm{87.20\pm 4.74}$ | $\bm{79.20\pm 5.41}$

In this section, we evaluate performance of the proposed FH-TabNet
architecture through a $5$-fold CV experiment. Given the imbalanced nature of
the data, we employ stratified CV to guarantee that each fold retains the same
class distribution as the original dataset. In our experiment, FH-TabNet
underwent training for $250$ epochs, with a step learning rate decay strategy
starting from $9e-2$ with a step size of $50$ and a decay factor of $0.9$.
Both stages have identical values for $n_{a}$ and $n_{d}$, with values of $40$
and $50$, respectively, for the first and second stages. The F1-score is
utilized as the primary metric due to data’s inherent imbalance, allowing to
better showcase the model’s efficacy within this specific context. Table I
presents the $5$-fold CV results of the proposed FH-TabNet framework,
encompassing Stage-$1$, Stage-$2$(P), and Stage-$2$(H). Performance of
Stage-$1$ is indeed commendable, as evidenced by the F1-score reported in
Table I. Furthermore, Stage-$1$ achieved Accuracy of $98.8$%, Sensitivity of
$99.6$%, and Specificity of $91.4$. We can conclude that Stage-$1$ effectively
distinguishes between two categories, i.e., Healthy (“Unlikely” and
“Possible”), and Patient (“Probable” and “Definite”). Healthy Class
constitutes approximately $90.7\%$ of the dataset, while Patient Class
accounts for approximately $9.3\%$. This underscores Stage-$1$’s impressive
performance, illustrating its ability to effectively handle and classify data
even in the face of such class distribution disparities. Furthermore, when we
examine the F1-scores across the five folds in Stage-$1$, we observe
consistently high values, ranging from $0.91$ to $1$. These results
demonstrate the model’s ability to perform well across different subsets of
the data, further validating its effectiveness in classifying the distinct
categories. The predicted classes by Stage-$1$ are given to the parallel
binary classification models, where Stage-$2$(H) distinguishes between the
“‘Unlikely” and “Possible” FH categories. Remarkably, owing to the substantial
dataset presence in this category, this stage exhibits outstanding
performance, as indicated by F1-scores ranging from $0.97$ to $0.99$ across
various folds. Finally, Stage-$2$(P) classifies “Probable” and “Definite” FH
patients. Considering the fact that a small portion of data lies in this
classification, the F1-score of the Stage-$2$(P) classifier is less than the
Stage-$2$(H). As a final note, we report AUC values associated with the FH-
TabNet. For Stage-$2$(H), the AUC values are $0.99$, $0.99$, $0.99$, $0.99$,
and $0.99$ and for Stage-$2$(P), the values are $0.95$, $0.87$, $0.84$,
$0.91$, and $0.85$, respectfully for folds $1$-$5$.

Additionally, we compare the performance of the proposed FH-TabNet framework
particularly in terms of the average F1-score across $5$-fold CV, with several
established traditional ML models and a single-stage multi-class TabNet
framework. These models encompass RF, LR, Linear Discriminant Analysis (LDA),
Ridge, AdaBoost, and K-Nearest Neighbors (KNN) classifiers. According to the
information provided in Table II, it is evident how well the models perform in
predicting the “Unlikely “and “Possible” categories, which are the dominant
categories due to their higher sample count. It should be noted that all
models, including the FH-TabNet, excel in predicting “Unlikely” and “Possible”
FH classes. However, what sets the FH-TabNet framework apart is its
exceptional performance in predicting the “Probable” and “Definite” classes.
The primary reason behind this remarkable performance lies in the multi-step
binary classification approach adopted within the FH-TabNet framework dealing
with tabular data. This innovative approach results in a more balanced
distribution of data in both Stage-$2$(P) and Stage-$2$(H), which, in turn,
leads to significantly improved multi-class predictions, as evidenced by the
substantially higher F1-scores in all classes. Finally, we analyzed high-
weight features by aggregating their scores and ranking them to identify the
most pivotal features in classifying sub-categories of FH. Our findings reveal
that LDL-Code-FH, cardiovascular disease, or a history of diagnosis stand out
as the most important features in classifying “Possible” and “Unlikely”,
supported by their respective average scores of $0.33$, $0.13$, and $0.11$
across different folds. Similarly, in classification of “Definite” and
“Probable”, we identified the pivotal features as Corneal Arcus, history of
cancer in the family, and LDL-Code-FH, which exhibit importance average weight
scores of $0.25$, $0.16$, and $0.08$, respectively. These scores signify the
aggregated importance of these features, showcasing their consistent
significance in the classification process across multiple evaluation
instances.

##  VI Conclusion

The paper introduced an innovative classification framework, referred to as
the FH-TabNet, designed specifically for tabular data related to a genetic
disorder known as Familial Hypercholesterolemia (FH), which is characterized
by high levels of LDL cholesterol in the blood leading to an increased risk of
cardiovascular problems. The FH-TabNet framework aims to provide precise
predictions regarding the risk stage of FH (Definite, Probable, Possible, and
Unlikely FH). The FH-TabNet model, as proposed, accomplishes this goal through
a multi-stage application of binary classification methods. Initially, we
streamlined the task by merging the Definite and Probable classes into one
category and the Possible and Unlikely classes into another, thereby
transforming the problem into binary classification, distinguishing between FH
and healthy patients. Following this, we employed two parallel binary
classification models for each subcategory, leading to a more detailed
refinement of the FH risk stage. The FH-TabNet has notably enhanced the
dependability of FH risk prediction when contrasted with conventional ML
models. It has achieved notably higher F1-scores, particularly in the
prediction of the low-prevalence subcategory of FH patients.

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