Adolescent behavioral risk analysis and prediction using machine learning: a foundation for precision suicide prevention

Suicidal ideation, attempts, and deaths among adolescents are a major and growing health concern. In 2019, suicide accounted for 11% of adolescent deaths in the U.S. (second-leading cause of death among U.S. teenagers). Accurately predicting suicidal thoughts and behaviors (STBs) among adolescents remains challenging. This study aimed to identify the most accurate prediction models for adolescent STBs using machine learning (ML) methods. The predictors were selected by expert-informed and parametric models. The study used the data from Mississippi Youth Risk Behavior Surveillance System (YRBSS). The data were collected from Mississippi public high school students between 2001 and 2019 (inclusive). A broad array of features (survey question responses) were available to train the models including depression, drug use, bullying, violence, online habits, diet, and sports participation. We applied support vector machine (SVM), random forest, and neural network algorithms to the YRBSS data. Suicide ideation (consideration) or suicide attempt are used as the outcome variables. Data-derived ML models performed well in predictive accuracy. These results are compared with three ML algorithms versus three different methods of predictor variable selection. The highest accuracy was achieved with expert-informed models. The accuracy of predicting suicide ideation was slightly higher than the accuracy of suicide attempt. The difference between ML algorithms was insignificant. These prediction models of suicide ideation and attempt may help Mississippi public high schools educators, parents, and policy makers, better target risk behaviors and hence effectively prevent adolescent suicide in Mississippi.