Long-term Alzheimer's disease mortality prediction in adults aged ≥60 years: A prospective cohort study benchmarking survival machine learning algorithms.

ABSTRACT:

Introduction

Accurate risk stratification for long-term Alzheimer's disease (AD)-specific mortality remains limited.

Methods

We analyzed data from 5,149 adults aged ≥60 years in NHANES III (1988-1994), with 116 baseline variables and mortality follow-up through 2019 via the National Death Index. Ten survival machine learning (ML) models were benchmarked. Predictive performance was assessed using Harrell's concordance index (C-index).

Results

Over a median follow-up of 12.1 years for survivors and 17.8 years for decedents, Lasso (C-index = 0.76, 95% CI: 0.72-0.80) and Extreme Gradient Boosting (C-index = 0.76, 95% CI: 0.73-0.79) achieved the highest accuracy. Feature importance analyses revealed novel predictors of AD mortality. Models using fewer than 20 variables ret

SUBMITTER: Huang X 

PROVIDER: S-EPMC12703662 | biostudies-literature | 2025 Oct-Dec

REPOSITORIES: biostudies-literature