Enhancing Fairness and Accuracy in Diagnosing Type 2 Diabetes in Young Adult 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 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 paper identifies that deficiency in traditional machine learning models and proposes an algorithm to mitigate the bias towards the young population when predicting diabetes. Deviating from the traditional concept of one-model-fits-all, the method trains customized machine-learning models for each age group. The proposed solution consistently improves recall of the diabetes class by 26% to 40% in the young age group and outperforms 7 commonly used whole-group resampling techniques by at least 36% in terms of diabetes recall in the young age group.

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