Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database.

ABSTRACT:

Objectives

To explore the heterogeneous disability trajectories and construct explainable machine learning models for effective prediction of long-term disability trajectories and understanding the mechanisms of predictions among the elderly Chinese at community level.

Methods

This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018. A total of 4149 subjects aged 65 + in 2002 with completed activities of daily living (ADL) information for at least three waves were included. The mixed growth model was used to identify disability trajectories, and five machine learning models were further established to predict disability trajectories using epidemiological variables. An explainable approach was deplo

SUBMITTER: Wu Y 

PROVIDER: S-EPMC9336105 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature