基于机器学习预测儿童和青少年遭受暴力受害的风险

Background Violence victimisation among United States (USA) youth is a major public health concern, with over 6 600 homicides and 320 000 non-fatal assault-related emergency visits among 10–24 years old in 2022. This study aims to identify socio-ecological risk factors and explore the use of machine learning to predict the risk of violence victimisation, defined as a broad indicator of exposure to crime, violence or assault occurring within a 12-month period among children and adolescents. Method We used seven machine learning algorithms, including support vector machines (SVM), random forest