=Paper=
{{Paper
|id=Vol-3910/aics2024_p05
|storemode=property
|title=Mitigating Bias in Medical Datasets: A Comparative Analysis of Generative Adversarial Networks (GANs) Based Data Generation Techniques
|pdfUrl=https://ceur-ws.org/Vol-3910/aics2024_p05.pdf
|volume=Vol-3910
|authors=Mohamed Ashik Shahul Hameed,Asifa Qureshi,Abhishek Kaushik
}}
==Mitigating Bias in Medical Datasets: A Comparative Analysis of Generative Adversarial Networks (GANs) Based Data Generation Techniques==
https://ceur-ws.org/Vol-3910/aics2024_p05.pdf
Mitigating Bias in Medical Datasets: A Comparative
Analysis of Generative Adversarial Networks (GANs)
Based Data Generation Techniques⋆
Mohamed Ashik Shahul Hameed1,∗,† , Asifa Mehmood Qureshi1,∗,† and Abhishek Kaushik1,∗,†
1
Regulated Software Research Centre (RSRC), Dundalk Institute of Technology, Dundalk, Ireland
Abstract
The increasing use of Artificial intelligence (AI) in the medical domain has highlighted a critical issue: bias in
datasets. Biases in medical datasets can lead to skewed predictions, unfair clinical decisions, incorrect diagnoses
and poor generalisation of AI models. Very often, these biases are the consequence of imbalance in the dataset.
Generative Adversarial Networks (GANs) have appeared to be a promising solution for solving the data imbalance
issue. Synthetic data can help mitigate bias by balancing the dataset for sensitive attributes as well as for class
labels. However, the efficiency of different GAN variants in mitigating bias remains unexplored in the medical
domain. This paper investigates and compares various GAN variants to identify the most effective approach to
producing balanced data. In this study, we evaluated different variants of GAN on three medical datasets with the
aim of contributing to the development of more fairer and inclusive AI models in the medical domain. The study
shows that the performance of the Machine Learning (ML) model improves when the dataset is balanced using
synthetic data samples. Moreover, the MedGAN variant performs better when compared with other variants of
GAN.
Keywords
Bias, fairness, medical datasets, GANs, TGAN, CTGAN, MedGAN, MC-MedGAN
1. Introduction
Bias in Artificial Intelligence (AI) models refers to AI systems that produce biased results that reflect and
amplify human prejudices within a community, encompassing past and contemporary social injustices
[1]. These biases when replicated in medical datasets can have life-threatening consequences due to
incorrect diagnosis or treatment recommendations [2, 3]. For example, German researchers built a
skin cancer detection system using neural networks in 2016. The system was able to detect 95% of
melanoma cases accurately. It was trained on 10,000 skin images and outperformed 58 dermatologists.
Later, it was found that the data was highly dominated by white skin images and did not generalise
well to a diverse population [4]. These biases can be handled at pre-processing, algorithmic level or
in the post-processing stages of an AI model development [5]. Handling bias would help achieve fair
models that do not discriminate against different groups and treat them unfairly [6]. Pre-processing
techniques involve handling bias at the data level. One of the widely used techniques to mitigate bias
is over-sampling. Over-sampling is the generation of synthetic data that mirrors the characteristics
of real-world data. It helps to reduce bias by balancing the representation of different demographic
groups so that machine learning models produce reasonable outcomes and generalise well over a
diverse population [7]. There are several techniques to generate synthetic data to ensure fairness in
medical datasets [8]. These techniques include SMOTE [9], FairSMOTE [10], BorderlineSMOTE [11],
and Cluster-based over-sampling [12]. Moreover, deep learning is also widely used to generate artificial
data because of its high efficiency and accuracy in generating data. The most commonly used algorithm
is the Generative Adversarial Network (GANs) that have gained immense popularity in the research
AICS’24: 32nd Irish Conference on Artificial Intelligence and Cognitive Science, December 09–10, 2024, Dublin, Ireland
∗
Corresponding author.
†
These authors contributed equally.
$ D00253306@student.dkit.ie (M. A. S. Hameed); asifa.mehmood@dkit.ie (A. M. Qureshi)
0009-0009-3062-2971 (M. A. S. Hameed); 0009-0002-4312-353X (A. M. Qureshi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
community [13].
GAN is a deep learning model that mainly consists of two neural networks: a Generator used to
generate artificial data and a discriminator that tries to distinguish between real and synthetic data
to improve quality. These models were first introduced to process only image data, but later different
variants of GAN were proposed to process tabular data as well. These variants include Tabular GAN
(TGAN) [14], Conditional Tabular GAN (CTGAN) [15], Medical GAN (MEDGAN) [16], Multi-Categorical
GAN (MC-MedGAN) [17] and many more.
In this study, we evaluated various GAN variants including GAN, TGAN, CTGAN, MedGAN, and
MC-MedGAN to generate synthetic samples to balance different group representations within medical
datasets. The newly balanced dataset was fed into different ML models including Logistic Regression
(LR), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN) to draw a comparison.
The GAN models are evaluated on three different medical datasets that consist of gender as a sensitive
attribute to balance: the Asthma Disease Dataset [18], the Heart Disease Prediction Dataset [19], and
the Cancer Prediction dataset [20]. The performance is evaluated using various metrics i.e., accuracy,
precision, F1-score, recall, and Area Under Curve (AUC) scores. Fairness is evaluated using Equal
Opportunity (EO) [21], Propensity Score (PS) [22], and Statistical Parity (SP) [23].
2. Motivation
In today’s world, AI is an integral part of the healthcare system. The AI model must incorporate
transparency and accountability. The goal of this research is to reduce bias in medical datasets that
contain inherent biases due to unequal representation of different demographic groups. AI models
can become unfair and imbalanced, particularly in the healthcare sector, where underrepresented
groups may receive scant care. Bias in medical datasets poses a significant challenge to the reliability
of predictive models [24]. This could be critical for healthcare systems since an automated model
prediction has a direct effect on patients that affects their mental health, and quality of life or may
risk the life of an individual [25] as well it also leads to financial loss [26]. Due to an unbiased dataset,
certain populations may receive incorrect diagnoses or treatments as a result of unreliable predictions
brought on by bias in datasets. Nonetheless, GANs provide a potentially helpful way to generate AI
data that can assist in balancing underrepresented groups in health databases. The aim to explore how
GAN-based techniques can eliminate bias through data augmentation and enable more reliable and
equitable Machine Learning (ML) models motivates this effort [13]. The comparative study’s main goal
is to identify the optimal variant to lessen bias in medical datasets. We want to improve the quality
of treatment by lowering bias and ensuring that AI systems generate reliable, accurate, and equitable
forecasts for a range of demographics. Therefore, the motivation of this study is to investigate different
variants of GAN including TGAN, CTGAN, MedGAN, and MC-MedGAN for their efficacy in mitigating
bias and improving predictive performance on multiple medical datasets. This work will serve as a
foundation for further experimentation on data generation via GAN to mitigate biases.
Hypothesis: GAN-based data generation methods can help to reduce biases and ensure fairness in
medical datasets.
The formulated research questions to explore the above hypothesis are as follows:
• Does GAN-based synthetic data generation help reduce biases in medical datasets? If yes, which
GAN variant performs better among basic GAN, TGAN, CTGAN, MedGAN, and MC-MedGAN?
The rest of the article is structured as follows: Section 3 highlights some of the recent related work.
Section 4 explains the methodology in detail. Section 5 explains the results. Section 6 discusses the
hypothesis and research questions and Section 7 concludes the discussion with future work.
3. Related work
GANs have gained significant attention in recent years due to their capability of generating high-
quality data. Therefore, this section reviews the recent methodologies that leverage GAN models to
generate synthetic data. A study [27] presents the potential of GANs in generating synthetic data
from observational health data and discusses some of the unique challenges associated with healthcare
datasets, such as concerns about class imbalance. Observational Health Data (OHD) is highly valuable
for medical research and health informatics. The use of such data is severely limited because of strict
regulations. It highlights that GAN-generated synthetic data can help overcome some of the common
challenges, such as bias, privacy and class imbalance. The authors argue that GANs are useful in
generating healthcare data to combat the scarcity of high-quality medical datasets. Moreover, to address
the challenges of drift and class imbalance of gas detection systems, [28] employed CTGAN for data
augmentation. The result shows a significant improvement in the classification accuracy of each class
for both Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) thus reducing bias toward
the majority class. They conclude that CTGAN provides a feasible solution to generate a balanced
dataset.
In another study [29] various variants of GAN including CTGAN, TGAN, and Wasserstein GAN
(WGAN) are utilised for the anonymisation of real data through data synthesis. These models were
compared for precision, recall, and coverage scores to evaluate the generation of realistic tabular data,
handling missing and class-imbalanced data, and ensuring privacy. The results show that, although
no GAN method performs best in each evaluation metric, CTGAN and TGAN produce better scores
in most of the evaluation metrics. Additionally, in [30] a new variant of GAN called Multi-label Time-
series GAN (MTGAN) is proposed to generate sequential Electronic Health Record (EHR) data using
a gated recurrent unit with a smooth conditional matrix, while the critic evaluates temporal features
using Wasserstein distance for improving the quality of synthetic data. The results show that MTGAN
generates realistic EHR data effectively and improves accuracy for uncommon diseases.
The above studies show that GANs have the potential to generate high-quality diverse datasets that
can be used to handle bias in real-world datasets. Therefore, to analyse the capabilities of different GAN
variants, this study aims to conduct multiple experiments and then assess the fairness within the newly
generated synthetic medical datasets.
4. Methodology
Figure 1 shows the systematic methodology diagram used to evaluate the different variants of GANs.
First, the data is preprocessed and split into standard train and test sets. Then, the data is fed into the
GAN variant to generate synthetic data. The newly generated data is augmented with the real data to
balance the number of samples for the sensitive attributes and the output label. Afterwards, ML models
are trained on the newly generated data to evaluate the overall performance as well as the fairness of
the models.
4.1. Preprocessing
The data preprocessing includes one hot encoding to replace categorical variables with numerical
numbers. Afterwards, we applied z-score normalisation on each distinct numerical feature because they
did not contain extreme outliers [31]. Normalisation helps to specify each variable within a specified
range to simplify the model-learning process [32]. Then, the resulting dataset is split into a 70:30 ratio
for train and test sets.
4.2. Generate synthetic data
In order to balance the dataset for the sensitive attribute i.e., Gender and class labels. We employed
five GAN architectures: basic GAN, TGAN, CTGAN, MedGAN and MC-MedGAN. These variants are
Figure 1: Systematic Methodology Diagram to Evaluate GAN Variants
specifically designed to handle tabular and medical datasets which is the primary focus of our study.
GAN is a type of neural network architecture where two networks, a generator, and a discriminator,
are trained simultaneously [33, 34, 35]. Tabular GAN is an application-driven variant of the GAN that
is designed to generate synthetic tabular data, containing rows and columns like in a spreadsheet or
database [33, 14, 36]. The CTGAN is an extension to Tabular GAN that generates synthetic tabular data
while taking into consideration the distribution of dependent target variables. This will help associate
relations between columns and observe dependence relationships [15]. MedGAN is a specialised version
of GAN that generates synthetic data in the medical field, mainly in tabular form containing sensitive
information [16]. MC-MedGAN is a variant of MedGAN designed for handling multi-categorical
variables, commonly present in medical datasets [17].
4.3. Train ML classifiers
After generating synthetic samples to balance the datasets for sensitive attribute (gender) and class
labels, different commonly used ML classifiers including Logistic regression (LR), Random Forest (RF),
Decision Tree (DT), K-Nearest Neighbour (KNN) with default parameters are trained on the newly
generated datasets to evaluate the performance of GAN variants.
4.4. Datasets
To evaluate the performance of GAN variants, we used three different medical datasets that contain
sensitive attributes. The details of each of these datasets are as follows:
Asthma Disease Dataset: The Asthma Disease Dataset [18] contains a record of 2,392 samples with
28 features. The output label is the diagnosis indicator, which is taken as 0 for the absence and 1 for a
positive case. It contains 2,268 samples for class 0 as compared to 124 samples with class label 1. Also,
the number of samples for males is 1212 whereas for females the count is 1180.
Heart Disease Prediction Dataset: The Heart Disease Prediction Dataset [19] consists of 13 features
and 303 samples. The dataset contains 207 male and 96 female samples.
Cancer Prediction Dataset: The Cancer Prediction Dataset [20] contains 1,500 samples with 8
features. The target variable ’diagnosis’ indicates whether a patient has cancer or not (0 for no cancer
and 1 for cancer). The diagnosis distribution shows 943 patients without cancer and 557 with cancer.
There are 736 female samples and 764 males in total.
5. Results
The performance is evaluated by training different ML classifiers as mentioned in Section 4. The
classifiers are assessed using accuracy, f1-score, precision, recall and AUC. Whereas the fairness of the
dataset is evaluated via EO, PS, and SP. EO guarantees that all individuals receive the same treatment
and meet the same requirements [21]. PS can be defined as the conditional probability of being exposed
to a treatment given the observed covariates [22]. SP is a fairness criterion that requires the probability
of a favourable outcome to be the same for each demographic group [23]. Tables 1, Table 2, and Table
3 show each classifier’s performance on the original as well as on each generated dataset. It can be
seen that MedGAN performs well for the Asthma Disease Dataset and Cancer Prediction Dataset while
MC-MedGAN has a better score for the Heart Disease Dataset.
Table 1
Accuracy, F1-score, Precision, Recall and AUC score comparison over the Asthma Disease Dataset
Method Model Accuracy F1-score Precision Recall AUC
LR 0.4989 0.4594 0.4951 0.4285 0.4996
RF 0.4926 0.5050 0.4901 0.5210 0.4628
Original Dataset
DT 0.4864 0.4029 0.4770 0.3487 0.4559
KNN 0.4926 0.4840 0.4892 0.4789 0.4903
LR 0.5381 0.5283 0.5600 0.5000 0.5487
RF 0.7124 0.6967 0.7667 0.6383 0.7912
GAN
DT 0.6662 0.6862 0.6680 0.7053 0.6648
KNN 0.5958 0.6128 0.6074 0.6183 0.6047
LR 0.5147 0.4892 0.5380 0.4485 0.5254
RF 0.7267 0.7064 0.7967 0.6345 0.7797
TGAN
DT 0.6689 0.6932 0.6666 0.7221 0.6669
KNN 0.5691 0.5878 0.5827 0.5929 0.5912
LR 0.5103 0.4952 0.5428 0.4553 0.5119
RF 0.7425 0.7248 0.8309 0.6427 0.7920
CTGAN
DT 0.6563 0.6952 0.6532 0.7429 0.6509
KNN 0.5597 0.5720 0.5871 0.5577 0.5786
LR 0.5272 0.5140 0.5472 0.4845 0.5425
RF 0.7409 0.7233 0.8054 0.6563 0.7883
MedGAN
DT 0.6715 0.6980 0.6640 0.7356 0.6694
KNN 0.5756 0.5898 0.6114 0.5695 0.5858
LR 0.5134 0.5128 0.5167 0.5134 0.5084
RF 0.6927 0.6916 0.7015 0.6927 0.7635
MC-MedGAN
DT 0.6226 0.6172 0.6238 0.6226 0.6220
KNN 0.5711 0.5706 0.5705 0.5711 0.6062
Figure 2, shows the fairness metric performance on the Asthma Disease dataset. The SP, PS, and
EO scores improve when the dataset is balanced for class label and gender. MEDGAN has a better
performance for all three datasets followed by MC-MedGAN and TGAN. The same performance is
observed for the other two datasets. The other graphs are given in Appendix A.
Overall, the results show that balancing the dataset for class labels and sensitive attributes improves
the performance as well as the fairness of the model. Among different GAN variants, the MEDGAN
produces good results and lower statistical, propensity and equal opportunity scores showing its great
capability for reducing bias followed by MC-MedGAN. Moreover, the predictive ability of RF classifiers
is better than other classifiers in terms of accuracy, precision, recall, f1-score, and AUC.
Table 2
Accuracy, F1-score, Precision, Recall and AUC score comparison over the Heart Disease Prediction
Dataset
Method Model Accuracy F1-score Precision Recall AUC
LR 0.7049 0.8125 0.8125 0.8125 0.6522
RF 0.8032 0.8775 0.8600 0.8958 0.7996
Original Dataset
DT 0.6885 0.7654 0.9393 0.6458 0.8261
KNN 0.7868 0.8631 0.8723 0.8541 0.7203
LR 0.7126 0.8166 0.8567 0.8957 0.7039
RF 0.8915 0.9010 0.9318 0.8723 0.9621
GAN
DT 0.8915 0.8988 0.9523 0.8510 0.8977
KNN 0.8674 0.8791 0.9090 0.8510 0.9255
LR 0.7349 0.8441 0.8205 0.8808 0.7352
RF 0.8915 0.9010 0.9318 0.8723 0.9621
TGAN
DT 0.9277 0.9333 0.9767 0.8936 0.9329
KNN 0.8674 0.8791 0.9090 0.8510 0.9137
LR 0.7250 0.8153 0.8500 0.9217 0.6876
RF 0.9083 0.9197 0.9264 0.9130 0.9766
CTGAN
DT 0.8250 0.8292 0.9444 0.7391 0.8975
KNN 0.8750 0.8800 0.9821 0.7971 0.9903
LR 0.7108 0.8891 0.8355 0.8734 0.7340
RF 0.9036 0.9130 0.9333 0.8936 0.9598
MedGAN
DT 0.8674 0.8791 0.9090 0.8510 0.9021
KNN 0.8674 0.8791 0.9090 0.8510 0.9284
LR 0.7746 0.9096 0.9510 0.8382 0.7133
RF 0.9277 0.9347 0.9555 0.9148 0.9728
MC-MedGAN
DT 0.9277 0.9333 0.9767 0.8936 0.9320
KNN 0.8433 0.8539 0.9047 0.8085 0.9414
Table 3
Accuracy, F1-score, Precision, Recall and AUC score comparison over the Cancer Prediction Dataset
Method Model Accuracy F1-score Precision Recall AUC
LR 0.6000 0.5714 0.5882 0.5555 0.6846
RF 0.6133 0.5671 0.6129 0.5277 0.6588
Original Dataset
DT 0.5666 0.5608 0.5460 0.5763 0.6028
KNN 0.6366 0.5657 0.6635 0.4930 0.6586
LR 0.6218 0.5776 0.5944 0.6431 0.6898
RF 0.7527 0.7580 0.7500 0.7661 0.8453
GAN
DT 0.6890 0.7154 0.6656 0.7733 0.6881
KNN 0.7090 0.7359 0.6798 0.8021 0.8002
LR 0.6549 0.5891 0.5920 0.5921 0.6958
RF 0.7271 0.7478 0.7317 0.7647 0.8331
TGAN
DT 0.6849 0.7304 0.6676 0.8062 0.6774
KNN 0.7161 0.7574 0.6914 0.8373 0.8369
LR 0.6696 0.6458 0.5958 0.5735 0.7680
RF 0.7287 0.7349 0.7375 0.7323 0.8342
CTGAN
DT 0.7269 0.7409 0.7224 0.7605 0.7260
KNN 0.6690 0.7053 0.6498 0.7711 0.7836
LR 0.7745 0.6698 0.5899 0.6563 0.7057
RF 0.7071 0.7028 0.7230 0.6836 0.7852
MedGAN
DT 0.6795 0.7119 0.6534 0.7818 0.6782
KNN 0.7071 0.7371 0.6757 0.8109 0.8123
LR 0.7452 0.5923 0.5977 0.7090 0.7694
RF 0.7005 0.7011 0.7116 0.6909 0.7782
MC-MedGAN
DT 0.6635 0.6862 0.6524 0.7236 0.6625
KNN 0.6543 0.6867 0.6366 0.7454 0.7558
(a)
(b)
(c)
Figure 2: Fairness Assessment for Asthma Disease dataset (a) Statistical Parity (b) Propensity Score, (c) Equal
Opportunity
6. Discussion
This section discusses the overall findings of the study in view of the literature review and extensive
experimentation conducted to analyse our hypothesis. Based on our research question, the experiments
show that classifier performance as well as the fairness metrics score improves when the datasets are
balanced for sensitive attributes and class labels. Figure 2 shows the improvement in the fairness scores
across each metric when the dataset is balanced via synthetic data generation using GAN variants as
compared to the original dataset. Moreover, the analysis of each GAN variant based on performance
evaluation using accuracy, precision, F1-score, recall, AUC and fairness metrics via EO, PS, and SP
indicates that the MedGAN produces efficient performance followed by MC-MEDGAN across all three
datasets. To validate any statistically significant difference between these two methods, we applied a
paired t-test on the EO, PS, and SP scores for each of these methods. The p-values for EO, PS, and SP
came out to be 0.34, 0.61, and 0.30 respectively. Therefore, we fail to reject our hypothesis and conclude
that these two methods are not significantly different. These GAN variants are specifically designed for
medical datasets to capture the interdependencies between the different variables to generate synthetic
data similar to original data properties [16, 17]. However, further experimentation with other datasets
including post-hoc tests will be conducted in future to provide deeper insights into the capability of
GAN variants for data generation.
7. Conclusion and future work
In this paper, we tested different types of GANs for their capacity to produce synthetic tabular data
to decrease bias in medical datasets. Our key findings are GAN-based models are effective for bias
migration and GAN can provide a balanced dataset to produce generalised AI models and provide a
solution AI for all and AI for good. On the other hand, traditional GANs were successful but medical
domain-based GANs displayed greater performance in generating high-quality and unbiased data. It
drives us to have more specific models in the future. Despite certain advantages of the GAN, we face
some obstacles such as evaluation metrics. There is a need to have more standardised and compressive
evaluation metrics of this model focused on decreasing bias. The studies in this article suggest that
synthetic data can assist in eliminating bias and improve the effectiveness of the classifier. Moreover,
MedGAN performs better in terms of SP, PS, and EO. In future, we will extend our work for various
variations of GAN focused on refining GAN architecture to adapt the multimodality medical data,
bias-sensitive evaluation mechanism and testing the GAN-based techniques in real-world clinical data.
Acknowledgments
This research was managed by the CREATE-DkIT project, supported by the HEA’s TU-Rise programme
and co-financed by the Government of Ireland and the European Union through the ERDF Southern,
Eastern Midland Regional Programme 2021-27 and the Northern Western Regional Programme 2021-27.
This research is also partially supported by the Research Ireland under Grant Number 21/FFP-A/9255.
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A. Fairness Assessment Graphs
(a)
(b)
(c)
Figure 3: Fairness Assessment for Heart Disease Prediction dataset (a) Statistical Parity (b) Propensity Score, (c)
Equal Opportunity
(a)
(b)
(c)
Figure 4: Fairness Assessment for Cancer Prediction dataset (a) Statistical Parity (b) Propensity Score, (c) Equal
Opportunity