Development and validation of a two-stage machine learning model for personalised type 2 diabetes screening in the All of Us Research Program and UK Biobank.

ABSTRACT:

Objective

To develop and externally validate a two-stage machine learning framework that integrates polygenic risk and clinical variables for early identification of individuals at risk of developing type 2 diabetes.

Methods

We conducted a prospective prediction study using data from the All of Us Research Program for model development and the UK Biobank for external validation. Two models were constructed. Stage 1 used gradient boosted decision trees (XGBoost) with cross validation, automated hyperparameter optimisation and class weighting to predict 5-year incident type 2 diabetes using demographic, clinical and polygenic predictors. Stage 2 incorporated glycated haemoglobin or fasting glucose measurements to refine risk estimates. Model interpretation used SHapley Additiv

SUBMITTER: Khattab A 

PROVIDER: S-EPMC12853511 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature