Undersampling for Fairness: Achieving More Equitable Predictions in Diabetes and Prediabetes

While type 2 diabetes is predominantly found in the elderly population, recent publications indicate an increasing prevalence in the young adult population. Failing to diagnose it in the minority younger age group could have significant adverse effects on their health. Several previous works acknowledge the bias of machine learning models towards different gender and race groups and propose various approaches to mitigate it. However, those works failed to propose any effective methodologies to diagnose diabetes in the young population, which is the minority group in the diabetic population. This is the first paper where we mention digital ageism towards young adult population diagnosing diabetes. In this paper, we identify this deficiency in traditional machine learning models and propose an algorithm to mitigate the bias towards the young population when predicting diabetes. Deviating from the traditional concept of one-model-fits-all, we train customized machine-learning models for each age group. Our proposed solution consistently improves recall of diabetes class by 26% to 40% in the young age group (30-44). Moreover, our technique outperforms 7 commonly used whole-group sampling techniques such as random oversampling, SMOTE, and AdaSyns techniques by at least 36% in terms of diabetes recall in the young age group. We also analyze the feature importance to investigate the source of bias in the original model. We tested our approach on multiple datasets using multiple machine learning models and multiple sampling algorithms. Our code is publicly available at an anonymous repository - https://anonymous.4open.science/r/Diabetes-BRFSS-DP-C847