This study applies machine learning to analyze socio-ecological and psychological risk factors for suicide among Ghanaian junior high school students. Findings reveal key predictors, including depression and parental support, and demonstrate the potential of AI/ML to enhance culturally relevant, data-driven interventions for adolescent suicide prevention in low- and middle-income countries.
Learning Objectives:
At the end of this session, attendees should be able to:
Upon completion, the participant will be able to: Understand the application of machine learning techniques to analyze socio-ecological and psychological risk factors for adolescent suicide in LMICs.
Upon completion, the participant will be able to: Identify key predictors of suicide risk, including depression, anxiety, and parental support, based on a nationally representative sample of Ghanaian students.
Upon completion, the participant will be able to: Explore the potential of AI/ML to inform culturally relevant, data-driven interventions for suicide prevention in resource-limited settings.
