ORIGINAL RESEARCH article
Front. Public Health
Sec. Aging and Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1628493
This article is part of the Research TopicEnhancing Geriatric Care with AI: Strategies for Fall Prevention and Aging-in-PlaceView all articles
Predicting Fall Risk Among Older Adults in Chinese Communities with Advanced Machine Learning Techniques: a retrospective study
Provisionally accepted
- 1Wuhan Union Hospital, Wuhan, China
- 2First Affiliated Hospital of Guangxi Medical University, Guangxi, China
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Background: This study aims to develop a advanced machine learning model to predict the fall risk among community-dwelling elders. This study could present actionable advices for early prevention of fall risk. Methods: Between October and December 2022, 977 elderly residents from the Hannan District of Wuhan were recruited. Data was collected using structured questionnaires. The sample was randomly split into training (732 participants) and testing (245 participants) sets at a 3:1 ratio. The primary outcome was the occurrence of fall. Five machine learning models—Random Forest (RF), Gradient Boosted Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Categorical Features Gradient Boosting (CatBoost)—were evaluated against a Logistic Regression (LR) model. Model performance was assessed using AUC, accuracy, precision, sensitivity, specificity, and F1 score. Results: Among the 977 elderly individuals, 195 experienced falls (20.0%). ROC curve analysis showed AUC values of LR, RF, LGBM, GBDT,XGBoost and CatBoost were respectively 0.8390, 0.8632, 0.8614, 0.8544, 0.8705, and 0.8719. CatBoost had the highest AUC, indicating the best predictive performance. SHapley Additive exPlanations (SHAP) analysis identified key features influencing the CatBoost model: history of falls, comorbidities, polypharmacy, sleep disorders, ADL, TUG results, frailty status, and use of assistive devices. Conclusions: The fall risk prediction model for community-dwelling elderly individuals, developed with CatBoost, showed excellent performance and can aid in early clinical assessment and fall prevention.
Keywords: Elderly, falls, machine learning, predictive model, SHAP Algorithm
Received: 23 May 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Liu, Zhang, Qu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Lianlian Qu, Wuhan Union Hospital, Wuhan, China
Debin Huang, First Affiliated Hospital of Guangxi Medical University, Guangxi, China
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