Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach.

Aims: Non-suicidal self-injury (NSSI) is a serious issue that is increasingly prevalent among children and adolescents, especially in rural areas. Developing a suitable predictive model for NSSI is crucial for early identification and intervention.

Methods: This study included 2090 Chinese rural children and adolescents. Participants' sociodemographic information, symptoms of anxiety as well as depression, personality traits, family environment and NSSI behaviors were collected through a questionnaire survey. Gender, age, grade, and all survey results except sociodemographic information were used as relevant factors for prediction. Support vector machines, decision tree and random forest models were trained and validated by the train set and valid set, respectively. The metrics of each model were tested and compared to select the most suitable one. Furthermore, the mean decrease Gini index was calculated to measure the importance of relevant factors.

Results: The prevalence of NSSI was 38.3%. Out of the 6 models assessed, the random forest model demonstrated the highest suitability in predicting the prevalence of NSSI. It achieved sensitivity, specificity, AUC, accuracy, precision, and F1 scores of 0.65, 0.72, 0.76, 0.70, 0.57, and 0.61, respectively. Anxiety and depression were the top two contributing factors in the prediction model. Neuroticism and conflict were the factors that contributed the most to personality traits and family environment, respectively, in terms of prediction. In addition, demographic factors contributed little to the prediction in this study.

Conclusion: This study focused on Chinese children and adolescents in rural areas and demonstrated the potential of using machine learning approaches in predicting NSSI. Our research complements the application of machine learning methods to psychiatric and psychological problems.