Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.

ABSTRACT:

Background

Frailty is a public health concern linked to falls, disability, and mortality. Early screening and tailored interventions can mitigate adverse outcomes, but community settings require tools that are accurate and explainable. Korea is entering a super-aged phase, yet few approaches have used nationally representative survey data.

Objective

This study aimed to identify key predictors of frailty risk using the K-FRAIL scale using explainable machine learning (ML), based on data from the 2023 National Survey of Older Koreans (NSOK). It also sought to develop and internally validate prediction models. To demonstrate the potential applicability of these models in community public health and clinical practice, a web-based application was implemented.

Methods

Data

SUBMITTER: Kim S 

PROVIDER: S-EPMC12629939 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature