An Artificial Intelligence Platform to Stratify the Risk of Experiencing Sleep Disturbance in University Students After Analyzing Psychological Health, Lifestyle, and Sports: A Multicenter Externally Validated Study

Abstract

Lirong Zhang,1 Shaocong Zhao,1 Zhongbing Yang,2 Hua Zheng,3 Mingxing Lei4,5 1Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, 361024, People’s Republic of China; 2School of Physical Education, Guizhou Normal University, Guizhou, 550025, People’s Republic of China; 3College of Physical Education and Health Sciences, Chongqing Normal University, Chongqing, 401331, People’s Republic of China; 4National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing, 100039, People’s Republic of China; 5Department of Orthopedic Surgery, Chinese PLA General Hospital, Beijing, 100039, People’s Republic of ChinaCorrespondence: Hua Zheng, College of Physical Education and Health Sciences, Chongqing Normal University, No. 37, Middle Road, University Town, Shapingba District, Chongqing, 401331, People’s Republic of China, Tel +86+15923028254, Email [email protected] Mingxing Lei, Department of Orthopedic Surgery, Chinese PLA General Hospital, Beijing, 100039, People’s Republic of China, Tel +86+18811772189, Email [email protected]: Sleep problems are prevalent among university students, yet there is a lack of effective models to assess the risk of sleep disturbance. Artificial intelligence (AI) provides an opportunity to develop a platform for evaluating the risk. This study aims to develop and validate an AI platform to stratify the risk of experiencing sleep disturbance for university students.Methods: A total of 2243 university students were included, with 1882 students from five universities comprising the model derivation group and 361 students from two additional universities forming the external validation group. Six machine learning techniques, including extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), neural network (NN), and support vector machine (SVM), were employed to train models using the same set of features. The models’ prediction performance was assessed based on discrimination and calibration, and feature importance was determined using Shapley Additive exPlanations (SHAP) analysis.Results: The prevalence of sleep disturbance was 44.69% in the model derivation group and 49.58% in the external validation group. Among the developed models, eXGBM exhibited superior performance, surpassing other models in metrics such as area under the curve (0.779, 95% CI: 0.728– 0.830), accuracy (0.710), precision (0.737), F1 score (0.692), Brier score (0.193), and log loss (0.569). Calibration and decision curve analyses demonstrated favorable calibration ability and clinical net benefits, respectively. SHAP analysis identified five key features: stress score, severity of depression, vegetable consumption, age, and sedentary time. The AI platform was made available online at https://sleepdisturbancestudents-xakgzwectsw85cagdgkax9.streamlit.app/, enabling users to calculate individualized risk of sleep disturbance.Conclusion: Sleep disturbance is prevalent among university students. This study presents an AI model capable of identifying students at high risk for sleep disturbance. The AI platform offers a valuable resource to guide interventions and improve sleep outcomes for university students.Keywords: university students, sleep disturbance, machine learning, artificial intelligence, feature importance

Keywords

• university students
• sleep disturbance
• machine learning
• artificial intelligence
• feature importance.