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

ABSTRACT:

Introduction

We aimed to develop risk and prognostic prediction tools for early-onset dementia (EOD) using health record data shared across five major international cohorts.

Methods

More than 400,000 dementia-free individuals younger than age 65 at baseline were included. Ensemble learning was used to construct the models. Cumulative incidence and Kaplan-Meier curves were used to visualize risk stratification, and subgroup analyses were conducted to evaluate potential disparities.

Results

The CatBoost-based risk model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.814 (<70 years) and 0.892 (<65 years). The Random Survival Forest (RF) prognostic model reached 5-year AUROC of 0.656. Key predictors included age, employment status, and edu

SUBMITTER: Rong X 

PROVIDER: S-EPMC12647925 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature