Real‐world prediction of early‐onset dementia by health record data: A multi‐center machine learning study

Abstract

We developed risk and prognostic prediction models for early-onset dementia (EOD) using indicators shared across five international cohorts. Models showed good discrimination and calibration across internal and external sets, with key predictors including age and work status confirmed by shapley additive explanation (SHAP) analysis. Subgroup analyses supported fairness across sex, age, and comorbidity groups. Our study provides accessible and cost-effective yet effective tools for the screening, prevention, and prognostic prediction of EOD in large community populations and primary care settings.

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