应用多种机器学习方法识别中国中学生非自杀性自我伤害行为的相关性：一项横断面研究

Background: Adolescent non-suicidal self-injury (NSSI) has become a major public health concern, with its rising prevalence driven by complex and interacting factors. Traditional analytical methods have limited capacity to capture non-linear relationships among high-dimensional variables. This study aimed to compare the performance of five machine learning algorithms in identifying NSSI and to determine its key correlates among Chinese adolescents. Participants and Setting: A total of 2,295 middle school students from Hebei and Shandong provinces in China, aged between 11 and 16 years, partici