Predicting all-cause mortality and premature death using interpretable machine learning among a middle-aged and elderly Chinese population.

ABSTRACT:

Objective

To develop machine learning-based prediction models for all-cause and premature mortality among the middle-aged and elderly population in China.

Method

Adults aged 45 years or older at baseline of 2011 from the China Health and Retirement Longitudinal Study (CHARLS) were included. The stacked ensemble model was built utilizing five selected machine learning algorithms. These models underwent training and testing using the CHARLS 2011-2015 cohort (derivation cohort) and subsequently underwent external validation using the CHARLS 2015-2018 cohort (validation cohort). SHapley Additive exPlanations (SHAP) was introduced to quantify the importance of risk factors and explain machine learning algorithms.

Result

In derivation cohort, a total of 10,677 subjects were

SUBMITTER: Yu Q 

PROVIDER: S-EPMC11399635 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature