Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach
Journal
Journal of neuroengineering and rehabilitation
Journal Volume
21
Journal Issue
1
Date Issued
2024-01-29
Author(s)
Ameri, Rasoul
Band, Shahab
Ho, Sung-Yu
Zaidan, Bilal
Chang, Kai-Chieh
Chang, Arthur
Abstract
Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability.
Subjects
Falls; Machine learning; Older adults; Risk classification; Trunk sway
Type
journal article