Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis

• URL: http://arxiv.org/abs/2607.16253v1
• Date: Sat, 27 Jun 2026 07:02:40 GMT
• Title: Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis
• Abstract summary: Machine learning-based Type 2 diabetes risk prediction models obtain good internal validation results but lose effectiveness in realworld due to deficient external testing and fairness assessment.<n>We developed a multi-dimensional framework evaluating discrimination, calibration, algorithmic fairness on nationally representative populations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening [23.49465719376556]
  Dysglycemia affects huge numbers of adults worldwide, yet many of them remain undiagnosed.<n>We developed and validated machine-learning models for non-invasive screening of dysglycemia risk that require no laboratory tests.
  arXiv  Detail & Related papers  (2026-06-14T23:10:55Z)
• Child Mortality Prediction in Bangladesh: A Decade-Long Validation Study [0.0]
  The Demographic and Health Surveys (DHS) data from Bangladesh for 2011-2022, with n = 33,962, are used in this paper.<n>We trained the model on (2011-2014) data, validated it on 2017 data, and tested it on 2022 data.<n>Eight years after the initial test of the model, a genetic algorithm-based Neural Architecture Search found a single-layer neural architecture to be superior to XGBoost.
  arXiv  Detail & Related papers  (2026-02-03T19:18:50Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods [0.8642326601683298]
  We developed a fair, rigorous, and reproducible MASLD prediction model.<n>MASLD affects 33% of U.S. adults and is the most common chronic liver disease.
  arXiv  Detail & Related papers  (2025-10-25T13:36:18Z)
• A SHAP-based explainable multi-level stacking ensemble learning method for predicting the length of stay in acute stroke [3.2906073576204955]
  Existing machine learning models have shown suboptimal predictive performance, limited generalisability, and have overlooked system-level factors.<n>We developed an interpretable multi-level stacking ensemble model for ischaemic and haemorrhagic stroke.<n>An explainable ensemble model effectively predicted the prolonged LOS in ischaemic stroke.<n>Further validation is needed for haemorrhagic stroke.
  arXiv  Detail & Related papers  (2025-05-30T01:08:26Z)
• Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV [49.1574468325115]
  This study explores multiple ML approaches for predicting LOS in ICU specifically for the patients with neurological diseases based on the MIMIC-IV dataset.<n>The evaluated models include classic ML algorithms (K-Nearest Neighbors, Random Forest, XGBoost and CatBoost) and Neural Networks (LSTM, BERT and Temporal Fusion Transformer)
  arXiv  Detail & Related papers  (2025-05-23T14:06:42Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification [71.06194656633447]
  We establish an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set. UIOS correctly predicted high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images.
  arXiv  Detail & Related papers  (2023-04-08T10:47:41Z)
• Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study [0.8620335948752806]
  We developed a predictive 10-year type 2 diabetes risk score using 301 features from the UK Biobank dataset. A Cox proportional hazards model slightly overperformed a DeepSurv model trained using the same features. This tool can be used for clinical screening of individuals at risk of developing type 2 diabetes and to foster patient empowerment.
  arXiv  Detail & Related papers  (2021-04-20T16:37:26Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.