AUTHOR=Wang Xia , Zhang Dan , Lu Liu , Meng Shujie , Li Yong , Zhang Rong , Zhou Jingjie , Yu Qian , Zeng Li , Zhao Jiang , Zeng Yu , Gao Ru 

TITLE=Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a cross-sectional study

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1619406

DOI=10.3389/fpubh.2025.1619406

ISSN=2296-2565

ABSTRACT=ObjectiveTo develop and validate an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity.MethodsA total of 471 older adults with multimorbidity were recruited between October and November 2024. We employed six machine learning (ML) methods, namely logistic regression (LR), neural network (NN), support vector machine (SVM), gradient boosting machine (GBM), K-Nearest Neighbors (KNN), and light gradient boosting machine (LightGBM), to predict the risk of sleep disorders based on their sociodemographic data, health behavior factors, mental health, and disease-related data. The optimal model was identified through the evaluation of the area under the curve (AUC). This study also employed explainable machine learning techniques to provide insights into the model’s predictions and outcomes using the SHAP (Shapley Additive Explanations) approach.ResultsThe prevalence of sleep disorders was 28.7%. Among the six models developed, the GBM model achieved the best performance with an AUC of 0.881. The analysis of feature importance revealed that the top seven predictors of sleep disorders were frailty, cognitive status, nutritional status, living alone, depression, smoking status, and anxiety.ConclusionThis study is the first to predict sleep disorders in Chinese older adults with multimorbidity using explainable machine learning methods and to identify seven significant risk factors. The SHAP method enhances the interpretability of machine learning models and helps medical staff better understand the rationale behind the predicted outcomes more effectively.