AUTHOR=Yao Ling , Chen Qingquan , Yang Kang , Zheng Zhihua , Chen Zhihan , Wang Danna , Xia Yining , Chen Dingquan , Chen Lufeng 

TITLE=Novel insight into prediction model for sleep quality among college students: a LASSO-derived sleep evaluation

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1585732

DOI=10.3389/fpsyt.2025.1585732

ISSN=1664-0640

ABSTRACT=BackgroundSleep disturbance has become a significant concern among college students, as it can lead to various mental and physical disorders. This study aims to provide a fresh perspective by developing and validating a predictive model for sleep quality among college students.MethodsData from 20,645 college students in Fujian Province, China, collected between 5th April and 16th April 2022, were analyzed. Participants completed the Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and a sleep quality influencing factor questionnaire. Multinomial logistic regression, LASSO regression, and Boruta feature selection methods were utilized to select relevant variables. The data were then divided into a training–testing set (70%) and an independent validation set (30%) using stratified sampling. Six machine learning techniques, including artificial neural network (ANN), decision tree, gradient-boosting tree, k-nearest neighbor, naïve Bayes, and random forest, were developed and validated. Finally, an online sleep evaluation website was established based on the best-fitting prediction model.ResultsThe mean global PSQI score was 6.02 ± 3.112, with a sleep disturbance prevalence of 28.9% (defined as a global PSQI score > 7). The LASSO regression model identified eight predictors: age, specialty, respiratory history, coffee consumption, staying up late, prolonged online activity, sudden changes, and impatient closed-loop management. Among the evaluated models, the ANN demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.713 (95% CI: 0.696–0.730), accuracy of 0.669 (95% CI: 0.669–0.669), sensitivity of 0.682 (95% CI: 0.699–0.665), specificity of 0.637 (95% CI: 0.665–0.610). Decision curve analysis and clinical impact analysis further confirmed the model’s clinical utility.ConclusionsThis study developed a prediction model for sleep disturbance among college students using a LASSO regression and ANN, incorporating eight predictors. The model can serve as an intuitive and practical tool for predicting sleep quality and supporting effective management and healthcare on college campuses.