Predicting Suicide or Self-Harm Crises Based on Decision Tree Analysis of Life Events and Coping Style: A Population-Based Study in China - Shumeng Ma; Ping Li; Liwen Ren; Ning Jia (SAGE Open)
Suicide and self-harm crises among high school students are significant public health issues. Previous research has often focused on individual factors in suicide and self-harm crises, neglecting the complex interactions between multiple factors. This study, based on the diathesis-stress model, utilized survey data of 12,472 Chinese high school students and employed machine learning methods to construct a decision tree model. It analyzed the most significant negative life events and coping styles in predicting suicide and self-harm crises, explored the impact of these factors on students, and examined sex differences. The classification tree’s built-in contribution function allowed us to obtain the importance of each variable. Results indicated that the model performed well, with the classification tree demonstrating strong predictive accuracy for self-harm and suicide crises among both male and female students. While the impact of negative life events and coping styles on suicide crises showed cross-sex consistency, sex differences were observed for self-harm crises. Among male students, only interpersonal relationships exceeded the 10% threshold in importance, whereas a wider range of events surpassed this threshold for female students. Coping styles played a critical role for both groups, further underscoring their importance in helping students mitigate crises amid negative events. The decision tree model demonstrated high accuracy in identifying students at risk of suicide and self-harm crises. Through the decision tree model, the study identified several key negative life events and coping styles, offering valuable insights for educators to provide more targeted attention and guidance in intervening in suicide and self-harm crises.