HUEY-WEN LIANGAmeri, RasoulRasoulAmeriBand, ShahabShahabBandHSIN-SHUI CHENHo, Sung-YuSung-YuHoZaidan, BilalBilalZaidanChang, Kai-ChiehKai-ChiehChangChang, ArthurArthurChang2024-04-082024-04-082024-01-291743-0003https://scholars.lib.ntu.edu.tw/handle/123456789/641785Computerized 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.enFalls; Machine learning; Older adults; Risk classification; Trunk swayjournal article10.1186/s12984-024-01310-3382874152-s2.0-85183580739https://api.elsevier.com/content/abstract/scopus_id/85183580739