Enhancing Fairness and Accuracy in Diagnosing Type 2 Diabetes in Young Population

While type 2 diabetes is predominantly found in the elderly population, recent publications indicate an increasing prevalence in the young adult population. Failing to predict it in the minority younger age group could have significant adverse effects on their health. The previous work acknowledges the bias of machine learning models towards different gender and race groups and proposes various approaches to mitigate it. However, prior work has not proposed any effective methodologies to predict diabetes in the young population which is the minority group in the diabetic population. In this paper, we identify this deficiency in traditional machine learning models and implement double prioritization (DP) bias correction techniques 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. The DP model consistently improves recall of diabetes class by 26 to 40 in the young age group (30-44). Moreover, the DP 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. Our code is publicly available at an anonymous reposi- tory - https://anonymous.4open.science/r/Diabetes-BRFSS- DP-C847