# Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions
of Diabetic Patient Readmissions

Zainab Al-Zanbouri  Computer Science Dept.
Toronto Metropolitan University
Toronto, ON, Canada
<EMAIL_ADDRESS>Gauri Sharma  Dept. of Electrical & Computer
Engineering
McGill University
Montreal, QC, Canada
<EMAIL_ADDRESS>Shaina Raza  AI Engineering
Vector Institute for Artificial Intelligence,
Toronto, ON, Canada
<EMAIL_ADDRESS>
###### Abstract

This study investigates how machine learning (ML) models can predict hospital
readmissions for diabetic patients fairly and accurately across different
demographics (age, gender, race). We compared models like Deep Learning,
Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes.
GBM stood out with an F1-score of 84.3% and accuracy of 82.2%, accurately
predicting readmissions across demographics. A fairness analysis was conducted
across all the models. GBM minimized disparities in predictions, achieving
balanced results across genders and races. It showed low False Discovery Rates
(FDR) (6-7%) and False Positive Rates (FPR) (5%) for both genders.
Additionally, FDRs remained low for racial groups, such as African Americans
(8%) and Asians (7%). Similarly, FPRs were consistent across age groups (4%)
for both patients under 40 and those above 40, indicating its precision and
ability to reduce bias. These findings emphasize the importance of choosing ML
models carefully to ensure both accuracy and fairness for all patients. By
showcasing effectiveness of various models with fairness metrics, this study
promotes personalized medicine and the need for fair ML algorithms in
healthcare. This can ultimately reduce disparities and improve outcomes for
diabetic patients of all backgrounds.

###### Index Terms:

Predictive and diagnostic analytics, Machine learning, Artificial
intelligence, Hyperglycemia, Health disparity, Accuracy.

##  I Introduction

Health disparities denote the substantial and preventable differences in
health outcomes or healthcare service access across various population groups.
These disparities arise from multifaceted influences, including socioeconomic,
environmental, and cultural factors, leading to inequitable health experiences
and outcomes among diverse communities. Manifestations of health disparities
are observable across several health indicators, such as disease prevalence,
morbidity, mortality, and access to healthcare and preventive services [1, 2,
3].

In the realm of ML , the concept of "fairness" pertains to the ethical and
equitable consideration of individuals or groups in the development,
deployment, and utilization of ML models [4]. Achieving fairness in ML
involves mitigating bias and discrimination within algorithmic decision-making
processes [5], a crucial step to prevent adverse impacts on specific
demographic groups and ensure equitable outcomes. Fairness in ML encompasses
various dimensions, including algorithmic fairness, data fairness, fair
treatment of individuals, and the principles of explainability and
transparency [6, 7, 8].

The role of big data in health science is increasingly recognized as critical,
especially as ML algorithms, developed from vast datasets, have the potential
to perpetuate or introduce new health disparities [9]. Bias in ML is generally
identified in relation to the dataset or model, with specific concerns around
labeling, sample selection, data retrieval, scaling, imputation, or model
selection biases [10]. Addressing these biases is crucial at three distinct
stages of the software development lifecycle: early (pre-processing), mid (in-
processing), and late (post-processing) [10, 11].

The early stage involves reducing bias by manipulating the training data
before algorithm training [12]. The mid-stage focuses on debiasing the model
itself, often through optimization problem-solving [13]. The late stage aims
to minimize biases by adjusting the output predictions post-training [14]. It
is noted that failing to identify and address biases early can limit the
effectiveness of bias mitigation strategies later in the ML pipeline [15, 16].
For instance, research suggests that analyzing historical diabetes care trends
in hospitalized patients can improve patient safety [17], and studies have
shown that HbA1c measurements are linked to lower hospital readmission rates
[18].

Fairness in ML is a crucial subject, aiming to develop unbiased models that
treat all individuals or groups equitably, without discrimination based on
sensitive attributes like gender, race, age, or ethnicity. This focus is
driven by ethical considerations, social impact, and the need for trust and
accountability in ML applications. Thus, ensuring fairness is essential for
the integrity of ML models.

The interest in bias and fairness in ML has surged among researchers, sparking
numerous studies addressing this issue, its challenges, and potential
solutions. Despite these efforts, a comprehensive understanding of fairness-
aware ML remains elusive. This work aims to bridge this gap by examining
racial/ethnic disparities that may affect hospitalization rates, specifically
aiming to reduce readmission disparities among diabetic patients by
considering factors such as gender, race, and ethnicity.

This paper contributions are twofold: 1\. We examine disparities in ML model
outputs concerning diabetic patient readmission rates. 2\. We conduct a
thorough analysis based on sensitive demographic groups—age, gender, and
race—to highlight key findings and implications.

Following this exploration, we aim to advance the understanding of fairness in
ML within the healthcare domain, particularly focusing on mitigating health
disparities among diabetic patients. Our research explores the intricate
mechanisms through which ML models may unintentionally exacerbate or magnify
discrepancies in hospital readmission rates, particularly focusing on the
influence of sensitive attributes like age, gender, and race. Through the
identification and rectification of these disparities, we aim to advance the
creation of healthcare models and algorithms that prioritize equity.

##  II Related Work

Pagano et al. [19] delve into bias and unfairness within ML models,
highlighting a focus on identification methods alongside existing metrics,
tools, datasets, and bias mitigation techniques aimed at fairness. Giovanola,
B. et al. [20] conceptualize fairness in AI ethics by integrating insights
from moral philosophy, thus redefining fairness in healthcare ML algorithms.
Gohar, U., et al. [21] provide a comprehensive review of intersectional
fairness, establishing a taxonomy for fairness concepts and mitigation
strategies, identifying challenges, and offering guidelines for future
research.

Chen, Z et al. [22] conduct an empirical study on bias mitigation methods for
ML classifiers, using a wide array of performance and fairness metrics to
assess the trade-offs in various software decision-making contexts. Their
findings indicate that while bias mitigation often leads to performance trade-
offs, it can also paradoxically reduce fairness in some scenarios. Pessach, D.
et al. [23] offer an overview of algorithmic fairness, discussing bias
origins, fairness definitions, and mitigation mechanisms, alongside a
comparative analysis of these mechanisms across different scenarios, enhancing
understanding of their applicability.

Pagano, T. P., et al. [24] explore bias and fairness metrics with a focus on
gender sensitivity in ML applications across computer vision, natural language
processing, and recommendation systems, underscoring the importance of context
in fairness metrics. Wan, M., et al. [25] review fairness mitigation
techniques, distinguishing between explicit methods, which integrate fairness
metrics into training objectives, and implicit methods, which refine latent
representations, highlighting ongoing challenges and encouraging further
research.

Yang, J., et al. [26] introduce an adversarial training framework aimed at
mitigating biases from data collection, showcasing its effectiveness in
improving fairness in COVID-19 predictions while maintaining clinical
efficacy. Wang, R., et al. [27] provide evidence that well-trained ML models
can exhibit unbiased performance across various subgroups, suggesting that
comprehensive training can overcome bias under multiple fairness metrics.

Wang, Z., et al. [28] propose a novel counterfactual approach to address bias
at its root, combining performance and fairness optimization to achieve
optimal outcomes in both domains. Their evaluation across benchmark tasks and
real-world datasets demonstrates the method’s ability to enhance fairness
without compromising performance. Collectively, these studies illuminate the
multifaceted challenges and solutions in advancing fairness and bias
mitigation in ML, underscoring the necessity of ongoing research and
innovation in this critical area.

Health Disparity: Health disparities, defined as systematic, preventable
differences in health outcomes that persist between distinct population
groups, have been a subject of considerable concern in public health research
[1]. These disparities manifest across various dimensions, including
socioeconomic status, race, ethnicity, gender, and geographical location. A
considerable body of literature underscores the role of social determinants of
health in driving disparities. Socioeconomic factors, such as income,
education, and employment, contribute significantly to differential access to
healthcare services and health outcomes [29]. Research has consistently
demonstrated pronounced health disparities among racial and ethnic groups.
Studies highlight disparities in chronic diseases, access to preventive care,
and maternal and child health outcomes among different racial and ethnic
communities [3]. Gender-based health disparities have also been well-
documented. Women and men often experience differences in the prevalence and
management of various health conditions. For example, studies reveal
disparities in cardiovascular health and mental health outcomes between
genders [30]. Geographical location plays a crucial role in health outcomes.
Rural populations, in particular, face challenges related to limited access to
healthcare facilities, healthcare workforce shortages, and socioeconomic
disparities [31]. The literature emphasizes the need for targeted
interventions and policy measures to address health disparities. Community-
based interventions, culturally competent healthcare, and policy initiatives
focusing on the social determinants of health are identified as essential
strategies [32]. Recent research explores the role of technology in mitigating
health disparities. Telehealth and mobile health applications, for instance,
have shown promise in improving healthcare access, particularly for
underserved populations [33].

In conclusion, the literature underscores the multifaceted nature of health
disparities and the imperative for comprehensive, interdisciplinary approaches
to address them. Future research should continue to explore innovative
strategies and interventions to reduce and ultimately eliminate health
disparities.

##  III Methodology

###  III-A Problem Definition

This research is primarily focused on investigating the equity of predictions
generated by ML models across various demographic categories, with a specific
emphasis on age, gender, and race. The study seeks to evaluate whether these
predictive models exhibit fairness and impartiality in their outcomes or if
there are disparities that disproportionately impact certain demographic
groups. By analyzing the predictive performance across different demographic
strata, the research aims to pinpoint potential biases and inequalities in
model predictions, thereby contributing to the development of more equitable
and unbiased ML algorithms.

The research question we aim to address is: Are the predictions of the models
equitable across diverse demographics, including age, gender, and race?

###  III-B Predictive Models

In this research, we conduct a comprehensive evaluation of the predictive
performance of various ML models, alongside a detailed analysis of their
fairness concerning demographic attributes such as age, gender, and race. Our
methodology involves the strategic selection of four distinct ML models, each
renowned for its specific capabilities and compatibility with the unique
facets of our dataset and overarching research goals. The models under
scrutiny include Deep Learning (DL), Gradient Boosting Machine (GBM),
Generalized Linear Model (GLM), and Naive Bayes (NB), chosen for their diverse
strengths in handling complex data and analytical challenges. Table I
summarizes the hyperparameter configurations for the used models.

Naive Bayes is a simple probabilistic classifier based on Bayes’ theorem with
the assumption of independence among features. It’s commonly used in machine
learning for classification tasks, especially in text categorization and spam
filtering. Despite its simplicity and the naive assumption of feature
independence, Naive Bayes often performs well in practice and is efficient in
both training and prediction.

Generalized Linear Model : GLM is a statistical framework used for modeling
relationships between a dependent variable and one or more independent
variables. It extends the ordinary linear regression model to accommodate
various types of response variables, including continuous, binary, count, and
categorical data. GLM incorporates a link function to connect the linear
predictor to the response variable, allowing for flexible modeling of non-
normal distributions. It is widely used in fields such as epidemiology,
biology, and social sciences for analyzing and interpreting data while
accounting for different types of distributions and relationships between
variables.

Gradient Boosting Machine:  GBM is a type of ML model that works by combining
multiple weak learners, typically decision trees, sequentially to improve
predictive performance. It builds these trees in a stepwise manner, with each
tree correcting the errors made by the previous ones, ultimately producing a
strong predictive model. GBM is known for its effectiveness in handling
structured data and is widely used in predictive modeling tasks such as
classification and regression.

Deep learning is a subset of ML that involves training artificial neural
networks with multiple layers to learn intricate patterns from data.
Multilayer Perceptron (MLP) is a versatile neural network architecture
commonly used in DL for capturing complex data patterns through its layered
structure.

TABLE I: Hyperparameter Configurations for Predictive Models

Model |  Detailed Hyperparameters
---|---
Naive Bayes |  Smoothing parameter (alpha): 1.0, Fit prior: True, Class prior: None
Generalized Linear Model |  Regularization type: Elastic Net, Regularization strength (alpha): 0.01, L1 ratio: 0.5, Convergence tolerance: 1e-4
Gradient Boosting Machine |  Learning rate: 0.1, Max tree depth: 5, Number of estimators: 100, Subsample: 0.8, Loss function: deviance
Multilayer Perceptron- MLP model |  Neurons per layer: [128, 64, 32] (indicating three hidden layers), Activation function: ReLU, Learning rate: 0.001, Optimization method: Adam, Batch size: 32, Epochs: 100

####  III-B1 Evaluation Strategy

Our evaluation strategy is multifaceted, beginning with an assessment of
predictive performance through key accuracy metrics and progressing to a
thorough fairness analysis to ensure equitable predictions across different
demographic groups.

Performance metrics [34] : In measuring model accuracy, we consider four
critical metrics. Precision is essential in scenarios where the cost of false
positives is high, as it measures the accuracy of positive predictions.
Recall, or sensitivity, is crucial for identifying the majority of actual
positives, significant where missing a positive case could be detrimental. The
F1 Score, a harmonic mean of precision and recall, offers a balanced metric,
particularly useful in situations with imbalanced datasets. Lastly, overall
model accuracy provides a general indication of performance, accounting for
both positive and negative predictions.

Fairness metrics: Our fairness analysis employs a comprehensive suite of
metrics aimed at uncovering any bias. The Disparate Impact Ratio examines
outcome disparities to highlight potential discrimination. The Predicted
Positive Rate (PPR) and Predicted Positive Group Rate (PPGR) assess the
distribution of positive predictions, aiding in detecting biases.
Additionally, the analysis includes the False Discovery Rate (FDR) and False
Positive Rate (FPR), focusing on the accuracy of positive predictions,
alongside the False Omission Rate (FOR) and False Negative Rate (FNR), which
evaluate the accuracy of negative predictions. The Group Size Ratio (GSR)
ensures proportional representation of various groups within the predictions.

Equity across these fairness metrics is our goal, aiming for lower error rates
(FDR, FPR, FOR, FNR) to indicate minimal inaccuracies and higher values for
PPR and PPGR, reflecting fair positive prediction distribution. An ideal GSR
value should be close to 1, indicating balanced group representation.

Our evaluation strategy is as:

  * •

Model Prediction Generation: Initiate the process by generating predictions
from the model for the entire dataset.

  * •

Accuracy Assessment: Evaluate the model’s predictive performance using
critical accuracy metrics, including Precision, Recall, F1 Score, and overall
Accuracy.

  * •

Sub-group Fairness Analysis:Employ fairness metrics such as Disparate Impact
and Equality of Opportunity to uncover any potential biases, ensuring the
model’s predictions are equitable across different demographics.

  * •

Sub-group Accuracy Evaluation: Within the fairness analysis framework, also
examine the accuracy metrics for each sub-group.

####  III-B2 Experimental setting

Our experimental setup consists of a Windows 11 computing system with an Intel
Core i9 processor, complemented by 8.00 GB of RAM, ensuring robust performance
for our research tasks. On the software front, Python 3.8 was used as
programming language, supported by a suite of libraries pivotal for ML:
scikit-learn (0.24.2) for a variety of algorithms, TensorFlow (2.6.0) and
PyTorch (1.9.0) for advanced deep learning models, alongside pandas (1.3.3),
NumPy (1.21.2), matplotlib (3.4.3), and seaborn (0.11.2) for data handling and
visualization.

###  III-C Cohort Selection

In this study, we utilized the diabetes dataset [35], providing a depiction of
clinical care across 130 US hospitals and integrated delivery networks
spanning from 1999 to 2008. The primary classification objective involves
predicting whether a patient is likely to be readmitted within 30 days.
Dataset Features: Comprising data from 101,766 patients, the dataset
encompasses 50 attributes, including 10 numerical, 7 binary, and 33
categorical features. The attributes encounter_id and patient_nbr are excluded
from the learning tasks as they serve as patient identifiers. Additionally,
attributes such as weight, payer_code, and medical_specialty are excluded due
to containing at least 40% missing values. Furthermore, we eliminate missing
values in the race, diag_1, diag_2, and diag_3 columns. The class label
"readmitted" initially contains 54,864 rows with the designation "no record of
readmission"; consequently, these rows are excluded as well. Following these
data processing steps, the refined dataset consists of 45,715 records. Class
Attribute: The class attribute is the "readmitted" attribute, denoted as ’<
30’ and ’> 30,’ indicating whether a patient is likely to be readmitted within
30 days. The positive class is represented by "< 30." The dataset exhibits an
imbalance with an Imbalance Ratio (IR) of 1:3:13 for the positive class to
negative classes.

##  IV Results

###  IV-A Quantifying overall model performance

Our study assesses ML models on a diabetes dataset, analyzing impacts by race,
gender, and age. We start with model predictions, evaluating accuracy through
metrics like accuracy, precision, recall, and F1 score. We then proceed to a
fairness analysis using metrics like GSR, PPR, PPGR, FDR, FPR, FOR, and FNR.
Additionally, we examine sub-group accuracy to ensure equitable performance.
Through this process, we iteratively refine our models to enhance both
accuracy and fairness, aiming to improve ML applications in healthcare, with a
focus on equitable diabetes care.

TABLE II: Comparative Analysis of Machine Learning Model Performance

Model name | Precision | Recall | F1 | Accuracy
---|---|---|---|---
NB | 0.778 | 0.989 | 0.772 | 0.740
GLM | 0.797 | 0.989 | 0.830 | 0.809
GBM | 0.815 | 0.998 | 0.843 | 0.822
MLP | 0.792 | 0.998 | 0.839 | 0.819

Our evaluation of machine learning model performance, detailed in Table II,
revealed GBM as the most comprehensive performer across the employed metrics
(precision, recall, F1-score, and accuracy). Notably, GBM achieved a well-
balanced F1-score of 84.3%, demonstrating its proficiency in correctly
identifying positive cases and capturing true positive cases. This balanced
performance is further validated by GBM’s accuracy of 82.2%, indicating its
ability to make accurate predictions. While other models exhibited strengths
in specific areas, they presented trade-offs. For instance, MLP achieved a
high precision of 79.2%, suggesting its effectiveness in minimizing false
positives. However, this strength came at the cost of a lower overall accuracy
compared to GBM. Similarly, GLM and NB demonstrated competitive recall,
indicating their ability to identify true positive cases. However, their lower
accuracy (80.9% for GLM and 74% for NB) suggests potential limitations in
correctly predicting positive cases overall.

###  IV-B Quantifying model performance disparities across gender groups

Table III provides a gender-wise (male, female) comparison of classifiers,
assessing their performance through key fairness metrics. This analysis aims
to highlight each model’s ability to achieve fairness and accuracy across
genders, offering insights into their effectiveness in minimizing bias and
maintaining equitable predictions.

TABLE III: Model Performance Metrics Across Gender Categories

Group | Metrics
---|---
Gender | Model | GSR$\uparrow$ | PPR$\uparrow$ | PPGR$\uparrow$ | FDR$\downarrow$ | FPR$\downarrow$ | FOR$\downarrow$ | FNR$\downarrow$
Female | NB | 0.54 | 0.56 | 0.37 | 0.18 | 0.13 | 0.31 | 0.40
GBM | 0.54 | 0.54 | 0.37 | 0.07 | 0.05 | 0.25 | 0.32
GLM | 0.55 | 0.54 | 0.39 | 0.09 | 0.07 | 0.24 | 0.29
MLP | 0.54 | 0.53 | 0.37 | 0.08 | 0.06 | 0.25 | 0.31
Male | NB | 0.46 | 0.44 | 0.35 | 0.18 | 0.12 | 0.31 | 0.42
GBM | 0.46 | 0.46 | 0.37 | 0.06 | 0.05 | 0.23 | 0.30
GLM | 0.45 | 0.46 | 0.40 | 0.10 | 0.08 | 0.24 | 0.29
MLP | 0.46 | 0.47 | 0.39 | 0.10 | 0.07 | 0.23 | 0.29

All models reflected the dataset’s inherent gender balance (GSR close to 0.5
for both genders), ensuring the models were not skewed by disproportionate
representation. However, there were some variations in how models treated the
two groups for positive predictions. The NB model exhibited a bias towards
females, with a higher PPR for females (56%) compared to males (44%). This
could be due to bias in the model or reflect real trends in the data. The MLP
model maintained a balanced approach with a slightly higher PPGR for males
(1.05) than females, suggesting the model considers gender in positive
predictions.

To evaluate overall accuracy and minimize errors, metrics like FDR and FPR
were analyzed. The GBM model achieved the lowest error rates (FDR: 7% females,
6% males; FPR: 5% for both genders), demonstrating its accuracy in minimizing
false positives and correctly identifying negative cases. On the other hand,
metrics like FOR and FNR measured how well models identified true positives.
The GLM performed better for females with a lower FOR (24%) and FNR (29%),
suggesting its efficiency in identifying true positives for females. However,
the NB model indicated it might be missing important information when making
predictions about females. (highest FOR: 31% females highest FNR: 40%
females),

###  IV-C Quantifying model performance disparities across race groups

Table IV provides a race- wise comparison of classifiers, assessing their
performance through different fairness metrics. This analysis is crucial for
understanding each model’s ability to deliver fairness and accuracy across
different racial groups (agrican-american, caucasian, hispanic, asian),
highlighting their effectiveness in minimizing bias and ensuring equitable
predictions.

TABLE IV: Model Performance Metrics Across Racial Groups

Group | Metrics
---|---
Race | Model | GSR$\uparrow$ | PPR$\uparrow$ | PPGR$\uparrow$ | FDR$\downarrow$ | FPR$\downarrow$ | FOR$\downarrow$ | FNR$\downarrow$
African American | NB | 0.19 | 0.20 | 0.36 | 0.20 | 0.14 | 0.31 | 0.40
GBM | 0.19 | 0.19 | 0.37 | 0.08 | 0.05 | 0.23 | 0.30
GLM | 0.19 | 0.19 | 0.39 | 0.11 | 0.08 | 0.24 | 0.29
MLP | 0.19 | 0.20 | 0.38 | 0.11 | 0.08 | 0.23 | 0.29
Asian | NB | 0.01 | 0.01 | 0.36 | 0.00 | 0.00 | 0.48 | 0.46
GBM | 0.01 | 0.01 | 0.58 | 0.00 | 0.00 | 0.20 | 0.12
GLM | 0.01 | 0.01 | 0.41 | 0.06 | 0.05 | 0.27 | 0.29
MLP | 0.01 | 0.01 | 0.56 | 0.00 | 0.00 | 0.25 | 0.17
Caucasian | NB | 0.77 | 0.77 | 0.36 | 0.18 | 0.13 | 0.31 | 0.41
GBM | 0.77 | 0.76 | 0.36 | 0.07 | 0.05 | 0.24 | 0.32
GLM | 0.77 | 0.77 | 0.39 | 0.09 | 0.07 | 0.24 | 0.29
MLP | 0.77 | 0.76 | 0.37 | 0.08 | 0.06 | 0.24 | 0.30
Hispanic | NB | 0.02 | 0.02 | 0.34 | 0.15 | 0.11 | 0.39 | 0.47
GBM | 0.02 | 0.02 | 0.39 | 0.04 | 0.04 | 0.28 | 0.32
GLM | 0.02 | 0.02 | 0.39 | 0.10 | 0.08 | 0.31 | 0.35
MLP | 0.02 | 0.02 | 0.38 | 0.13 | 0.11 | 0.35 | 0.39
Other | NB | 0.01 | 0.01 | 0.45 | 0.09 | 0.11 | 0.40 | 0.35
GBM | 0.01 | 0.02 | 0.50 | 0.08 | 0.11 | 0.34 | 0.27
GLM | 0.01 | 0.01 | 0.41 | 0.16 | 0.13 | 0.30 | 0.34
MLP | 0.01 | 0.02 | 0.50 | 0.08 | 0.11 | 0.34 | 0.27

While all models reflected the dataset’s inherent racial balance, some
variations emerged in positive predictions. The NB model exhibited a tendency
towards slightly higher positive predictions for certain groups, such as
African Americans (20% PPR), compared to other groups like Caucasians (19% PPR
in both MLP and GBM models). This suggests a potential bias in NB’s positive
prediction behavior. Conversely, the GBM model demonstrated a more balanced
approach, maintaining similar PPGR across racial groups.

GBM was a better performer in comparison to other models. It achieved the
lowest error rates, indicated by a low FDR of 8% for African Americans and 7%
for Caucasians. Additionally, GBM effectively identified negative cases with a
low FPR of 5% for both racial groups. In contrast, the NB model exhibited
higher error rates, particularly for African Americans (FDR of 20%),
suggesting a higher proportion of incorrect positive predictions. This trend
continued with identifying true positives. GBM performed well for African
Americans with a lower FOR of 23% and FNR of 30%. Conversely, the NB model
exhibited the most difficulty in accurately classifying true positives for
African Americans, evident from its higher FOR (31%) and FNR (40%).

###  IV-D Quantifying model performance disparities across age groups

Table V summarizes the age-wise performance of classifiers, evaluating their
effectiveness through different fairness metrics. This analysis seeks to shed
light on how well each model manages to ensure accuracy and fairness across
age groups (<40 and 40-99), pinpointing their capacity to avoid bias and
promote equitable outcomes.

TABLE V: Model Performance Metrics Across Different Age Groups

Group | Metrics
---|---
Age Group | Model | GSR | PPR | PPGR | FDR | FPR | FOR | FNR
<40 | MLP | 0.11 | 0 | 0 | undefined | 0 | 0.27 | 1
GBM | 0.11 | 0.01 | 0.04 | 0.54 | 0.03 | 0.26 | 0.93
GLM | 0.11 | 0.05 | 0.19 | 0.65 | 0.17 | 0.28 | 0.78
NB | 0.11 | 0.10 | 0.32 | 0.69 | 0.30 | 0.25 | 0.62
40-59 | MLP | 0.27 | 0.27 | 0.38 | 0.18 | 0.12 | 0.20 | 0.29
GBM | 0.27 | 0.22 | 0.29 | 0.07 | 0.04 | 0.24 | 0.38
GLM | 0.28 | 0.19 | 0.27 | 0.04 | 0.02 | 0.23 | 0.40
NB | 0.27 | 0.21 | 0.27 | 0.16 | 0.08 | 0.29 | 0.47
60-79 | MLP | 0.35 | 0.61 | 0.66 | 0.06 | 0.13 | 0.21 | 0.11
GBM | 0.35 | 0.64 | 0.66 | 0.07 | 0.14 | 0.21 | 0.11
GLM | 0.35 | 0.60 | 0.66 | 0.07 | 0.14 | 0.23 | 0.11
NB | 0.35 | 0.52 | 0.53 | 0.09 | 0.16 | 0.44 | 0.30
80-99 | MLP | 0.26 | 0.11 | 0.16 | 0.00 | 0.00 | 0.27 | 0.58
GBM | 0.26 | 0.13 | 0.19 | 0.03 | 0.01 | 0.26 | 0.54
GLM | 0.26 | 0.16 | 0.24 | 0.07 | 0.03 | 0.24 | 0.45
NB | 0.26 | 0.17 | 0.23 | 0.18 | 0.07 | 0.26 | 0.52

All models reflected a balanced representation of age groups in the data
(similar GSR across models for each age group). However, variations emerged in
PPR).The NB model tended to under-predict positive outcomes for younger
individuals (<40 years old, with a PPR of 10%), while GBM tended to predict
more positives for older adults (64% PPR in the 60-79 age group). This trend
is further supported by PPGR. Both MLP and GBM showed high PPGR values for the
60-79 age group (around 66%), suggesting a bias towards positive predictions
for older individuals.

Accuracy varied across models and age groups. MLP did not make any positive
predictions for the youngest age group, resulting in an undefined FDR.
Conversely, the GLM achieved a low FDR of 4% in the 40-59 age group,
indicating high accuracy in positive predictions for middle-aged adults.
Interestingly, MLP showed perfect accuracy in identifying negative cases for
the oldest age group (80-99 years old) with a FPR of 0

However, these findings also reveal potential shortcomings. The high FOR of
44% for NB in the 60-79 age group suggests it misses many true positive cases
among older adults. Similarly, the high FNR of 58% for MLP in the 80-99 age
group indicates a tendency to overlook true positives in the oldest
individuals.

TABLE VI: Best Performing Models Across Categories Category | Group | Better Performance Model | PPGR
---|---|---|---
Gender | Female | GLM | 39%
Male | GBM | 40%
Race | African American | GLM | 39%
Asian | GBM | 58%
Caucasian | GLM | 39%
Hispanic | GBM, GLM | 39%
Other | MLP, GBM | 50%
Age | 40-59 | MLP | 38%
60-79 | MLP, GBM, GLM | 66%
80-99 | GLM | 24%
<40 | NB | 32%

Summary: Table VI presents an overview of the highest-performing models across
various categories under study. In our analysis, the GLM emerged as a top
performer across several groups, achieving a PPGR of 39% in both gender
categories (female and male) and in the racial categories of African American,
Caucasian, and Hispanic. Furthermore, GLM demonstrated better performance in
comparison to other model, in the age groups of 60-79 and 80-99, with PPGRs of
66% and 24%, respectively, highlighting its broad applicability and
effectiveness across diverse demographic segments. The GBM, on the other hand,
showed better performance in the race categories of Asian, Hispanic, and
Other, with PPGRs of 58%, 39%, and 50%, respectively, and also excelled in the
60-79 age group with a PPGR of 66%. MLP was identified as the top model in the
’Other’ race category with a 50% PPGR and in the age categories of 40-59 and
60-79, with PPGRs of 38% and 66%, respectively, underscoring its effectiveness
in handling complex patterns in diabetes data. NB stood out in the age group
of under 40 with a PPGR of 32%, indicating its potential in early diabetes
detection among younger populations.

African American and Hispanic groups are affected by higher FDR and FPR rates
across several models, resulting in a higher likelihood of receiving false-
positive diagnoses. Notably, the NB model demonstrates a marked increase in
FDR and FPR for African Americans, raising concerns about the accuracy of
positive predictions for this demographic. While the Hispanic group encounters
somewhat lower FDR overall, models like MLP still exhibit relatively higher
FDR and FPR levels for this group, which may contribute to biased treatment
outcomes.

The GBM model excels in delivering fair outcomes across diverse demographic
segments. It exhibits consistently low rates of FDR and FPR for most racial
and gender categories, alongside maintaining competitive predictive power as
measured by the PPGR. Notably, when focusing on age, GBM, along with MLP and
GLM, presents a well-balanced approach. This is particularly evident within
the 60-79 age demographic, where it achieves a high PPGR, underscoring its
efficiency and fairness in forecasting diabetes-related results for a wide
range of patients.

Therefore, GBM emerges as the most equitable model overall, demonstrating a
consistent ability to minimize biases and ensure fair treatment across
different racial, gender, and age groups. This analysis underscores the
importance of choosing and refining ML models that not only perform accurately
but also uphold the principles of fairness and equity in healthcare
predictions and treatments.

##  V Discussion

Our study emphasizes the crucial role of ML in advancing the diagnosis,
treatment, and management of diabetes, a significant global health challenge.
Through our evaluation, GBM emerged as the most effective model across a
variety of performance metrics, including precision, recall, F1-score, and
accuracy. GBM’s balanced F1-score and commendable accuracy rate underscore its
proficiency in accurately identifying positive cases and making precise
predictions across different demographics.

This standout performance of GBM, however, is part of a larger narrative that
includes the differential efficacy of models such as GLM, GBM, and MLP across
diverse groups. For example, GLM shows enhanced performance for women and
African Americans, while GBM excels with Asians and Hispanics. Such variations
in model performance underscore the absence of a one-size-fits-all solution
and highlight the critical need for selecting and tailoring models based on
specific demographic groups to ensure accurate and equitable predictions.

The PPGR, a key finding of our study, further stresses the imperative for
fairness in ML predictions. The variance in PPGR across models and
demographics, especially with the lowest PPGR observed for the youngest age
group by NB, suggests areas for improvement in equitable disease
identification across age groups.

By identifying which models yield more accurate predictions for specific
demographic groups, healthcare providers can better customize care, moving
closer to the ideal of personalized medicine. The improved performance of GLM
for certain groups, for instance, suggests its potential for developing
targeted screening tools, thereby enhancing early diagnosis and treatment
effectiveness. Similarly, the effectiveness of GBM with other groups could
inform the creation of intervention programs that are more likely to engage
patients and improve outcomes.

Moreover, the general superiority of GBM across most metrics and groups,
particularly in gender and racial categorizations, affirms its robustness for
accurate and fair classification. This highlights the importance of model
selection and tuning to avoid bias and ensure equitable treatment,
significantly contributing to the advancement of personalized medicine and
tailoring interventions to individual patient profiles for improved healthcare
outcomes.

The gender-based analysis of ML models for diagnosing diabetes highlights
significant progress in achieving both technical accuracy and clinical
fairness, particularly through the use of GBM. GBM’s excellent performance,
characterized by low error rates and well-balanced fairness metrics for both
genders, demonstrates its effectiveness in providing accurate and unbiased
predictions critical for the diagnosis and treatment of diabetes. This is in
contrast to the NB model, which exhibited higher error rates, indicating
challenges in maintaining both accuracy and fairness and underscoring the
vital importance of precise model selection and calibration.

For women, GBM’s outstanding performance is marked by the lowest FDR and FPR,
showcasing its technical superiority in minimizing incorrect positive
diagnoses. This high level of precision has direct clinical implications,
leading to more dependable screenings and diagnoses for women, which is
crucial for reducing the risk of misdiagnosis and ensuring fair and accurate
care. GBM’s balanced PPR and PPGR further highlight its effectiveness in
providing equitable care, contributing to the reduction of gender disparities
in healthcare outcomes. The contrast with the higher error rates of the NB
model for women underscores the ongoing challenges in achieving fairness,
highlighting the need for meticulous model selection and tuning in healthcare
applications focused on women.

For men, GBM again demonstrates superior performance through minimal FDR and
FPR, underscoring its ability to accurately classify male patients and reduce
false positives. Such accuracy is essential for ensuring that men receive
appropriate and timely care for diabetes, which has a significant impact on
clinical outcomes by preventing both over-treatment and under-treatment. GBM’s
notable balance in PPGR and PPR metrics for males illustrates its capacity to
maintain fairness, ensuring that diagnostic and treatment decisions are free
from gender bias. Meanwhile, the increased likelihood of misclassification by
the NB model in the male category indicates potential biases, emphasizing the
importance of utilizing models like GBM that consistently exhibit high levels
of precision and fairness across genders.

Our race-based examination in the context of diabetes care with ML models
illuminates the critical importance of selecting models that cater well to
specific racial groups, thereby enhancing both accuracy and fairness in
healthcare outcomes. GLM and GBM stand out for their suitability across
different racial demographics, illustrating how strategic model choice can
contribute to reducing racial disparities in diabetes care. This approach
underscores a movement toward a healthcare system that is equitable, by
leveraging models that offer precision and fairness tailored to diverse racial
groups.

For African Americans, the adept performance of GLM, indicated by its ability
to effectively capture the unique healthcare needs of this demographic,
underscores the model’s potential in facilitating targeted and efficient
diabetes interventions. This tailored approach can significantly improve
clinical outcomes through early detection and management strategies that are
sensitive to the distinct risk profiles prevalent within the African American
community.

The Asian demographic benefits from the high predictive accuracy of GBM,
highlighting the model’s capability in addressing the specific healthcare
requirements of Asians. Such accuracy is vital for creating diabetes care
programs that are both culturally attuned and medically precise, thereby
fostering better patient engagement and adherence to treatment.

In the case of Caucasians, GLM’s adaptability, demonstrated by its effective
performance, plays a crucial role in fine-tuning diabetes care to align with
the epidemiological trends and healthcare needs of Caucasian patients. This
adaptability is key to advancing personalized healthcare, enhancing the
relevance and effectiveness of diabetes interventions.

The Hispanic demographic showcases the dual effectiveness of both GBM and GLM,
each achieving notable performance. This reflects the diversity within the
Hispanic community and signals the need for a healthcare approach that blends
data-driven insights with cultural sensitivity to optimize diabetes care
outcomes.

Lastly, the category labeled "Other" emphasizes the significance of
inclusivity and representation in healthcare data. The substantial performance
of both MLP and GBM models for this group points to the necessity of ensuring
ML models are trained on diverse datasets. This is crucial for minimizing
biases and improving the models’ capacity to deliver equitable and accurate
predictions across all racial groups, further advocating for the use of data
to foster a more inclusive healthcare environment

The age-based analysis of ML models for diabetes care reveals significant
insights into how different models can optimize healthcare delivery across
various age groups, demonstrating the importance of selecting age-appropriate
models to enhance accuracy and fairness in treatment. This analysis brings to
light models like MLP, GLM, GBM, and NB for their distinct effectiveness
across different age brackets.

For the younger population (under 40), the NB model stands out for its
efficacy, despite higher FDR and FPR compared to other age groups. This
effectiveness in predicting diabetes outcomes for younger individuals
accentuates the challenge of maintaining fairness and underscores the need for
model refinement to minimize bias and ensure that younger individuals receive
equitable healthcare attention.

In the middle-aged group (40-59), MLP models shine with their high PPGR,
balanced by relatively even FDR and FPR. These metrics suggest that MLP models
offer not only effectiveness but also fairness in identifying diabetes-related
outcomes among middle-aged individuals, highlighting the importance of bias-
free models that accurately reflect diabetes prevalence and ensure inclusive,
targeted interventions.

For older adults (60-79), the combined performance of MLP, GBM, and GLM
models, as indicated by their PPGR and fairness metrics (FDR and FPR),
showcases their ability to achieve both high accuracy and fairness. This dual
achievement is essential for minimizing the risks of over- or under-diagnosing
diabetes in older adults, suggesting that these models can significantly
contribute to fair and effective diabetes management strategies for an age
group that is particularly vulnerable to diabetes complications.

The eldest cohort (80-99) sees GLM as particularly effective, marked by high
accuracy and a low FNR, complemented by favorable fairness metrics (lower FDR
and FPR). This indicates that GLM provides a fair assessment of diabetes risk
among the elderly, crucial for avoiding unnecessary interventions or missing
treatment opportunities. This balance of accuracy and fairness is vital for
supporting the healthcare needs of the elderly, ensuring interventions are
both efficacious and equitable.

Overall, the age category conclusions reveal MLP and GLM as the most effective
models in two out of four age groups, specifically for the 40-59 and 60-79 age
brackets. GBM and NB each excel in one age group, with GBM suited for
individuals in the 60-79 age range and NB for those under 40. This
differentiation underscores the nuanced nature of model selection based on
age, pushing towards a healthcare paradigm that emphasizes personalized care
and mitigates age-related disparities in diabetes outcomes.

The broader implications of these findings underscore the practical impact of
fairness analysis in ML models, ranging from promoting equity and equitable
decision-making, mitigating bias, ensuring legal compliance, enhancing trust,
and fostering positive societal outcomes. It highlights the critical role of
ethical considerations in the development and deployment of AI technologies,
reinforcing the need for careful model selection and customization to meet the
diverse needs of patients across all age groups.

##### Limitations

Our study offers a detailed examination but has some limitations worth noting.
First, the reliance on conventional ML models may not capture the full
complexity of diabetes-related health outcomes across different demographic
groups, potentially oversimplifying the nuanced interplay of biological,
environmental, and social determinants of health. Additionally, the fairness
metrics used, though insightful, may not fully account for all dimensions of
bias and equity, particularly in the context of intersectionality where
multiple demographic factors overlap. The generalizability of findings is also
limited by the specific dataset and metrics employed, which may not be
representative of broader populations or encompass all relevant outcomes and
predictors of diabetes.

##### Future directions

Future work should aim to address these limitations through the incorporation
of more advanced techniques, such as advanced deep learning architectures and
ensemble methods, which may offer a more nuanced understanding of the patterns
and predictors of diabetes across diverse populations. Further exploration
into comprehensive fairness metrics and methodologies is also critical,
including the development of models that can account for and mitigate
multiple, intersecting biases. Expanding datasets to include a wider range of
demographic, socio-economic, and health-related variables would enhance the
robustness and applicability of findings. Lastly, integrating patient and
clinician perspectives could offer invaluable insights into the practicality,
acceptability, and ethical considerations of implementing ML models in
clinical settings, ensuring that future advancements not only push the
boundaries of technical feasibility but also align with the principles of
patient-centered and equitable care.

##  VI Conclusion

Our study reveals GBM as a standout in applying ML to diabetes care. Notably,
GBM demonstrates equitable performance across demographic lines. Both GBM and
GLM excel in delivering accurate and fair predictions, leading to improved
outcomes for patients of different genders, races, and ages. This progress
moves us closer to reducing disparities in diabetes care. However, challenges
remain, particularly for African American and Hispanic groups who experience
higher false positive rates. This highlights the importance of precise model
selection. The success of GBM and GLM underscores the potential for more
personalized and impactful healthcare interventions. Achieving this
necessitates an interdisciplinary healthcare approach. Merging ML with broader
medical expertise can deepen our understanding of diabetes and ensure a
healthcare system that is both fair and responsive to all patients’ needs. Our
findings advocate for refining these technological tools to ensure they
contribute positively to healthcare. Here, fairness and personalization should
be emphasized to guide future research towards a more equitable healthcare
model for diabetes.

## References

  * [1] P. Braveman and L. Gottlieb, “The social determinants of health: It’s time to consider the causes of the causes,” _Public Health Reports_ , vol. 129, no. 1_suppl2, p. 19–31, Jan. 2014. [Online]. Available: http://dx.doi.org/10.1177/00333549141291S206
  * [2] “The Root Causes of Health Inequity — ncbi.nlm.nih.gov,” https://www.ncbi.nlm.nih.gov/books/NBK425845/, [Accessed 04-03-2024].
  * [3] D. R. Williams and S. A. Mohammed, “Discrimination and racial disparities in health: evidence and needed research,” _J. Behav. Med._ , vol. 32, no. 1, pp. 20–47, Feb. 2009.
  * [4] S. Raza, P. O. Pour, and S. R. Bashir, “Fairness in machine learning meets with equity in healthcare,” in _Proceedings of the AAAI Symposium Series_ , vol. 1, no. 1, 2023, pp. 149–153.
  * [5] S. Raza and S. R. Bashir, “Auditing icu readmission rates in an clinical database: An analysis of risk factors and clinical outcomes,” in _2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)_. IEEE, 2023, pp. 722–726.
  * [6] D. R. Williams and S. A. Mohammed, “Racism and health i: Pathways and scientific evidence,” _American Behavioral Scientist_ , vol. 57, no. 8, p. 1152–1173, May 2013. [Online]. Available: http://dx.doi.org/10.1177/0002764213487340
  * [7] A. D. Selbst, D. Boyd, S. A. Friedler, S. Venkatasubramanian, and J. Vertesi, “Fairness and abstraction in sociotechnical systems,” in _Proceedings of the Conference on Fairness, Accountability, and Transparency_ , ser. FAT* ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 59–68. [Online]. Available: https://doi.org/10.1145/3287560.3287598
  * [8] B. Berendt and S. Preibusch, “Toward accountable discrimination-aware data mining: The importance of keeping the human in the loop—and under the looking glass,” _Big Data_ , vol. 5, no. 2, p. 135–152, Jun. 2017. [Online]. Available: http://dx.doi.org/10.1089/big.2016.0055
  * [9] S. Reddy, S. Allan, S. Coghlan, and P. Cooper, “A governance model for the application of ai in health care,” _Journal of the American Medical Informatics Association_ , vol. 27, no. 3, p. 491–497, Nov. 2019. [Online]. Available: http://dx.doi.org/10.1093/jamia/ocz192
  * [10] Aileen Nielsen, “Practical Fairness — oreilly.com,” https://www.oreilly.com/library/view/practical-fairness/9781492075721/, [Accessed 05-03-2024].
  * [11] S. Raza, “Connecting fairness in machine learning with public health equity,” in _2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)_. IEEE, 2023, pp. 704–708.
  * [12] F. Kamiran and T. Calders, “Data preprocessing techniques for classification without discrimination,” _Knowledge and Information Systems_ , vol. 33, no. 1, p. 1–33, Dec. 2011. [Online]. Available: http://dx.doi.org/10.1007/s10115-011-0463-8
  * [13] J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viegas, and J. Wilson, “The what-if tool: Interactive probing of machine learning models,” _IEEE Transactions on Visualization and Computer Graphics_ , p. 1–1, 2019. [Online]. Available: http://dx.doi.org/10.1109/TVCG.2019.2934619
  * [14] G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger, “On fairness and calibration,” 2017.
  * [15] L. Dixon, J. Li, J. Sorensen, N. Thain, and L. Vasserman, “Measuring and mitigating unintended bias in text classification,” in _Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society_ , ser. AIES ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 67–73. [Online]. Available: https://doi.org/10.1145/3278721.3278729
  * [16] K. Holstein, J. Wortman Vaughan, H. Daumé, M. Dudik, and H. Wallach, “Improving fairness in machine learning systems: What do industry practitioners need?” in _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ , ser. CHI ’19. ACM, May 2019. [Online]. Available: http://dx.doi.org/10.1145/3290605.3300830
  * [17] Mayo Clinic, “Hyperglycemia in diabetes-Hyperglycemia in diabetes - Symptoms & causes - Mayo Clinic — mayoclinic.org,” https://www.mayoclinic.org/diseases-conditions/hyperglycemia/symptoms-causes/syc-20373631, [Accessed 04-03-2024].
  * [18] B. Strack, J. P. DeShazo, C. Gennings, J. L. Olmo, S. Ventura, K. J. Cios, and J. N. Clore, “Impact of hba1c measurement on hospital readmission rates: Analysis of 70, 000 clinical database patient records,” _BioMed Research International_ , vol. 2014, p. 1–11, 2014. [Online]. Available: http://dx.doi.org/10.1155/2014/781670
  * [19] T. P. Pagano, R. B. Loureiro, F. V. N. Lisboa, G. O. R. Cruz, R. M. Peixoto, G. A. de Sousa Guimarães, L. L. dos Santos, M. M. Araujo, M. Cruz, E. L. S. de Oliveira, I. Winkler, and E. G. S. Nascimento, “Bias and unfairness in machine learning models: a systematic literature review,” 2022.
  * [20] B. Giovanola and S. Tiribelli, “Beyond bias and discrimination: redefining the ai ethics principle of fairness in healthcare machine-learning algorithms,” _AI & SOCIETY_, vol. 38, no. 2, p. 549–563, May 2022. [Online]. Available: http://dx.doi.org/10.1007/s00146-022-01455-6
  * [21] U. Gohar and L. Cheng, “A survey on intersectional fairness in machine learning: Notions, mitigation, and challenges,” in _Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence_ , ser. IJCAI-2023. International Joint Conferences on Artificial Intelligence Organization, Aug. 2023. [Online]. Available: http://dx.doi.org/10.24963/ijcai.2023/742
  * [22] Z. Chen, J. M. Zhang, F. Sarro, and M. Harman, “A comprehensive empirical study of bias mitigation methods for machine learning classifiers,” 2023.
  * [23] D. Pessach and E. Shmueli, “Algorithmic fairness,” 2020.
  * [24] T. P. Pagano, R. B. Loureiro, F. V. N. Lisboa, G. O. R. Cruz, R. M. Peixoto, G. A. d. S. Guimarães, E. L. S. Oliveira, I. Winkler, and E. G. S. Nascimento, “Context-based patterns in machine learning bias and fairness metrics: A sensitive attributes-based approach,” _Big Data and Cognitive Computing_ , vol. 7, no. 1, 2023. [Online]. Available: https://www.mdpi.com/2504-2289/7/1/27
  * [25] M. Wan, D. Zha, N. Liu, and N. Zou, “In-processing modeling techniques for machine learning fairness: A survey,” _ACM Transactions on Knowledge Discovery from Data_ , vol. 17, no. 3, p. 1–27, Mar. 2023. [Online]. Available: http://dx.doi.org/10.1145/3551390
  * [26] J. Yang, A. A. S. Soltan, D. W. Eyre, Y. Yang, and D. A. Clifton, “An adversarial training framework for mitigating algorithmic biases in clinical machine learning,” _npj Digital Medicine_ , vol. 6, no. 1, Mar. 2023. [Online]. Available: http://dx.doi.org/10.1038/s41746-023-00805-y
  * [27] R. Wang, P. Chaudhari, and C. Davatzikos, “Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies,” _Proceedings of the National Academy of Sciences_ , vol. 120, no. 6, Jan. 2023. [Online]. Available: http://dx.doi.org/10.1073/pnas.2211613120
  * [28] Z. Wang, Y. Zhou, M. Qiu, I. Haque, L. Brown, Y. He, J. Wang, D. Lo, and W. Zhang, “Towards fair machine learning software: Understanding and addressing model bias through counterfactual thinking,” 2023.
  * [29] Public Health Agency of Canada, “Social determinants of health and health inequalities - Canada.ca — canada.ca,” https://www.canada.ca/en/public-health/services/health-promotion/population-health/what-determines-health.html, [Accessed 05-03-2024].
  * [30] L. Mosca, E. Barrett-Connor, and N. K. Wenger, “Sex/gender differences in cardiovascular disease prevention: what a difference a decade makes,” _Circulation_ , vol. 124, no. 19, pp. 2145–2154, Nov. 2011.
  * [31] D. Hartley, “Rural health disparities, population health, and rural culture,” _Am. J. Public Health_ , vol. 94, no. 10, pp. 1675–1678, Oct. 2004.
  * [32] H. K. Koh, G. Graham, and S. A. Glied, “Reducing racial and ethnic disparities: the action plan from the department of health and human services,” _Health Aff. (Millwood)_ , vol. 30, no. 10, pp. 1822–1829, Oct. 2011.
  * [33] L. López, A. R. Green, A. Tan-McGrory, R. King, and J. R. Betancourt, “Bridging the digital divide in health care: the role of health information technology in addressing racial and ethnic disparities,” _Jt. Comm. J. Qual. Patient Saf._ , vol. 37, no. 10, pp. 437–445, Oct. 2011.
  * [34] P. Saleiro, B. Kuester, L. Hinkson, J. London, A. Stevens, A. Anisfeld, K. T. Rodolfa, and R. Ghani, “Aequitas: A bias and fairness audit toolkit,” 2019.
  * [35] B. Strack, J. DeShazo, C. Gennings, J. Olmo, S. Ventura, K. Cios, and J. Clore, “UCI machine learning repository: Diabetes 130-US hospitals for years 1999–2008 dataset,” https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008, 2014.