Ting Sun,1,2,* Jingfang Liu,3,* Hui Wang,3,* Bing Xiang Yang,3– 5 Zhongchun Liu,3 Jie Liu,6 Zhiying Wan,3 Yinglin Li,1 Xiangying Xie,1 Xiaofen Li,3 Xuan Gong,3 Zhongxiang Cai1 

1Department of Nursing, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 2Health Science Center, Yangtze University, Jingzhou, People’s Republic of China; 3Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China; 4School of Nursing, Wuhan University, Wuhan, People’s Republic of China; 5Population and Health Research Center, Wuhan University, Wuhan, People’s Republic of China; 6Anesthesiology, Virginia Commonwealth University Health System, Richmond, VA, USA

*These authors contributed equally to this work

Correspondence: Zhongxiang Cai, Department of Nursing, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, Hubei Province, 430060, People’s Republic of China, Email tg20201228@163.com Xuan Gong, Department of Psychiatry, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuhan, Hubei Province, 430060, People’s Republic of China, Email 12048387@qq.com

Background: Non-suicidal self-injury (NSSI) is a significant social issue, especially among adolescents with major depressive disorder (MDD). This study aimed to construct a risk prediction model using machine learning (ML) algorithms, such as XGBoost and random forest, to identify interventions for healthcare professionals working with adolescents with MDD.
Methods: This study investigated 488 adolescents with MDD. Adolescents was randomly divided into 75% training set and 25% test set to testify the predictive value of risk prediction model. The prediction model was constructed using XGBoost and random forest algorithms. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, recall, F Score of the two models for comparing the performance of the two models.
Results: There were 161 (33.00%) participants having NSSI. Compared without NSSI, there were statistically significant differences in gender (P=0.035), age (P=0.036), depressive symptoms (P=0.042), sleep quality (P=0.030), dysfunctional attitudes (P=0.048), childhood trauma (P=0.046), interpersonal problems (P=0.047), psychoticism (P) (P=0.049), neuroticism (N) (P=0.044), punishing and Severe (F2) (P=0.045) and Overly-intervening and Protecting (M2) (P=0.047) with NSSI. The AUC values for random forest and XGBoost were 0.780 and 0.807, respectively. The top five most important risk predictors identified by both machine learning methods were dysfunctional attitude, childhood trauma, depressive symptoms, F2 and M2.
Conclusion: The study demonstrates the suitability of prediction models for predicting NSSI behavior in Chinese adolescents with MDD based on ML. This model improves the assessment of NSSI in adolescents with MDD by health care professionals working. This provides a foundation for focused prevention and interventions by health care professionals working with these adolescents.

Keywords: non-suicidal self-injury, adolescents, major depressive disorder, risk prediction model