A machine learning-based fall-risk score for severity of fall-related adverse outcomes in community older adults.

ABSTRACT:

Background

Models that detect fall risk have been proposed. However, the value of an indicator derived from such models in fall-severity stratification is understudied. This study developed a machine learning (ML)-based fall classification model, constructed a fall-risk score, and explored its association with fall-related adverse outcomes.

Methods

We used the eXtreme Gradient Boosting algorithm to build a fall classification model using data from 15,457 community-dwelling adults aged 60 Years and older. Of the 216 fall-associated variables, the 15 most important variables were selected for modelling, and their directional relationships with falls were evaluated using the SHapley Additive exPlanation (SHAP) value. An ML-based fall-risk score (ML-FRS) was generated. Multileve

SUBMITTER: Chen H 

PROVIDER: S-EPMC12465683 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature