Mamun Mohammed A, Misti Jannatul Mawa, Hasan Md Emran, Al-Mamun Firoj, ALmerab Moneerah Mohammad, Islam Johurul, Muhit Mohammad, Gozal David
CHINTA Research Bangladesh, Dhaka 1342, Bangladesh.
Department of Public Health, University of South Asia, Dhaka 1348, Bangladesh.
Brain Sci. 2024 Oct 12;14(10):1015. doi: 10.3390/brainsci14101015.
Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. A cross-sectional study was conducted among 1496 adolescents using a structured questionnaire. Data were collected through a two-stage stratified cluster sampling method. Chi-square tests and logistic regression analyses were performed using SPSS. Machine learning models, including Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM), were employed to identify and predict EDS risk factors using Python and Google Colab. The prevalence of EDS in the cohort was 11.6%. SHAP values from the CatBoost model identified self-rated health status, gender, and depression as the most significant predictors of EDS. Among the models, GBM achieved the highest accuracy (90.15%) and precision (88.81%), while CatBoost had comparable accuracy (89.48%) and the lowest log loss (0.25). ROC-AUC analysis showed that CatBoost and GBM performed robustly in distinguishing between EDS and non-EDS cases, with AUC scores of 0.86. Both models demonstrated the superior predictive performance for EDS compared to others. The study emphasizes the role of health and demographic factors in predicting EDS among adolescents in Bangladesh. Machine learning techniques offer valuable insights into the relative contribution of these factors, and can guide targeted interventions. Future research should include longitudinal and interventional studies in diverse settings to improve generalizability and develop effective strategies for managing EDS among adolescents.
青少年日间过度嗜睡（EDS）对学业成绩、心理健康和整体幸福感构成重大风险。本研究调查了孟加拉国青少年中EDS的患病率和风险因素，并利用机器学习方法预测EDS风险。采用结构化问卷对1496名青少年进行了横断面研究。数据通过两阶段分层整群抽样方法收集。使用SPSS进行卡方检验和逻辑回归分析。利用Python和谷歌Colab，采用机器学习模型，包括分类提升（CatBoost）、极端梯度提升（XGBoost）、支持向量机（SVM）、随机森林（RF）、K近邻（KNN）和梯度提升机（GBM），来识别和预测EDS风险因素。该队列中EDS的患病率为11.6%。CatBoost模型的SHAP值确定自我评估的健康状况、性别和抑郁是EDS最显著的预测因素。在这些模型中，GBM的准确率最高（90.15%）和精确率最高（88.81%），而CatBoost的准确率相当（89.48%）且对数损失最低（0.25）。ROC-AUC分析表明，CatBoost和GBM在区分EDS和非EDS病例方面表现强劲，AUC分数为0.86。与其他模型相比，这两个模型在预测EDS方面均表现出卓越的性能。该研究强调了健康和人口因素在预测孟加拉国青少年EDS中的作用。机器学习技术为这些因素的相对贡献提供了有价值的见解，并可指导有针对性的干预措施。未来的研究应包括在不同环境下的纵向和干预性研究，以提高普遍性，并制定管理青少年EDS的有效策略。
