Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis
Summary
This paper develops a multi-dimensional framework to evaluate discrimination, calibration, interpretability, and algorithmic fairness for machine learning-based type 2 diabetes risk prediction models, revealing significant performance degradation under real-world distribution shifts and biases by age and obesity.
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# Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis Source: [https://arxiv.org/abs/2607.16253](https://arxiv.org/abs/2607.16253) [View PDF](https://arxiv.org/pdf/2607.16253) > Abstract:Machine learning\-based Type 2 diabetes risk prediction models obtain good internal validation results but lose effectiveness in real\-world applications due to deficient external testing and fairness assessment\. We developed a multi\-dimensional framework evaluating discrimination, calibration, interpretability, and algorithmic fairness on nationally representative populations\. An XGBoost model was trained on NHANES 2015\-2020 \(n=15,685\) using eight non\-laboratory predictors: age, sex, race/ethnicity, BMI, smoking status, physical activity, history of heart attack, and history of stroke\. External validation was performed on BRFSS 2020\-2022 \(n=1,285,783\) under realistic distribution shift\. Internal validation showed good discrimination \(AUC=0\.794, 95% CI 0\.788\-0\.800\), with performance loss on external validation \(AUC=0\.717, relative decrease: \-9\.7%, p<0\.001\)\. Fairness analysis revealed severe bias: elderly adults \(\>=60\) showed AUC=0\.607 vs 0\.742 for young adults \(difference=0\.135, p<0\.001\); obese individuals showed AUC=0\.698 vs 0\.735 for normal weight \(difference=0\.037, p<0\.001\)\. Gender showed comparable performance \(male=0\.723 vs female=0\.712, p=0\.142\)\. Calibration revealed risk overestimation \(Brier score=0\.123\)\. SHAP analysis identified age, BMI, and physical activity as primary risk drivers\. Populations with highest diabetes risk receive the worst algorithmic performance, underscoring the need for fairness\-aware, age\-stratified deployment strategies before clinical use\. ## Submission history From: Rajveer Singh Pall \[[view email](https://arxiv.org/show-email/d15c9dcd/2607.16253)\] **\[v1\]**Sat, 27 Jun 2026 07:02:40 UTC \(464 KB\)
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