Homicidality risk prediction based on ecological systems theory in an early adolescent cohort using machine learning

Abstract

Purpose

Identifying high-risk adolescents prone to homicidality, linked to serious criminal activities and homicide, offers vital avenues for homicide prevention.

Methods

This study analyzed data from 1596, 1596, and 1526 students at baseline, one-year, and two-year follow-ups, respectively, drawn from the Chinese Early Adolescent Cohort study. Based on Bronfenbrenner's ecological systems theory, predictors of adolescent homicidality were categorized into individual, family, and school and peer levels. Five machine learning methods were utilized to construct prediction models for homicidality risk and to pinpoint predictive factors.

Results

Logistic regression models using only significant features effectively predicted adolescent homicidality and new onsets in the short term, as well as homicidal trajectories throughout early adolescence. Key factors identified included suicidal ideation, emotional abuse, life satisfaction, physical violence, and verbal violence, with suicidal ideation and emotional abuse emerging as the most critical predictors.

Conclusions

This study successfully developed risk-predictive models for adolescent homicidality using machine learning, emphasizing suicidal ideation and emotional abuse as primary predictors. These findings highlight the importance of targeted interventions focused on these key variables for the early prevention of adolescent homicide.