Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource

Abstract

Jingsong Luo,1,2 Yuxin Chen,2 Yanmin Tao,1 Yaxin Xu,3 Kexin Yu,2 Ranran Liu,2 Yuchen Jiang,2 Cichong Cai,2 Yiyang Mao,2 Jingyi Li,2 Ziyi Yang,2 Tingting Deng1 1School of Nursing, The Chengdu University of Traditional Chinese Medicine, Sichuan, 610000, People’s Republic of China; 2Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China; 3School of Nursing, Tongji University, Shanghai, 200000, People’s Republic of ChinaCorrespondence: Tingting Deng, School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, People’s Republic of China, Email [email protected]: There is substantial evidence from previous studies that abnormalities in sleep parameters associated with depression are demonstrated in almost all stages of sleep architecture. Patients with symptoms of sleep-wake disorders have a much higher risk of developing major depressive disorders (MDD) compared to those without.Objective: The aim of the present study is to establish and compare the performance of different machine learning models based on sleep-wake disorder symptoms data and to select the optimal model to interpret the importance of sleep-wake disorder symptoms to predict MDD occurrence in adolescents.Methods: We derived data for this work from 2020 to 2021 Assessing Nocturnal Sleep/Wake Effects on Risk of Suicide Phase I Study from National Sleep Research Resource. Using demographic and sleep-wake disorder symptoms data as predictors and the occurrence of MDD measured base on the center for epidemiologic studies depression scale as an outcome, the following six machine learning predictive models were developed: eXtreme Gradient Boosting model (XGBoost), Light Gradient Boosting mode, AdaBoost, Gaussian Naïve Bayes, Complement Naïve Bayes, and multilayer perceptron. The models’ performance was assessed using the AUC and other metrics, and the final model’s predictor importance ranking was explained.Results: XGBoost is the optimal predictive model in comprehensive performance with the AUC of 0.804 in the test set. All sleep-wake disorder symptoms were significantly positively correlated with the occurrence of adolescent MDD. The insomnia severity was the most important predictor compared with the other predictors in this study.Conclusion: This machine learning predictive model based on sleep-wake disorder symptoms can help to raise the awareness of risk of symptoms between sleep-wake disorders and MDD in adolescents and improve primary care and prevention.Keywords: adolescent major depressive disorder, sleep-wake disorders symptom, insomnia, obstruct sleep apnea, machine learning prediction model, xgboost

Keywords

• adolescent major depressive disorder
• sleep-wake disorders symptom
• insomnia
• obstruct sleep apnea
• machine learning prediction model
• xgboost