Probabilistic Decision Trees for Predicting 12-Month University Students Likely to Experience Suicidal Ideation

Abstract

Environmental stressors combined with a predisposition to experience mental health problems increase the risk for SI (Suicidal Ideation) among college/university students. However, university health and wellbeing services know little about machine learning methods and techniques to identify as early as possible students with higher risk. We developed an algorithm to identify university students with suicidal thoughts and behaviours using features universities already collect. We used data collected in 2020 from the American College Health Association (ACHA), a cross-sectional population-based survey including 50, 307 volunteer students. A state-of-the-art parallel Markov Chain Monte Carlo (MCMC) Decision tree was used to overcome overfitting problems and target classes with fewer representatives efficiently. Two models were fitted to the survey data featuring a range of demographic and clinical risk factors measured on the ACHA survey. The first model included variables universities would typically collect from their students (e.g., key demographics, residential status, and key health conditions). The second model included these same variables plus additional suicide-risk variables which universities would not typically measure as standard practice (e.g., students’ sense of belonging at university). Models’ performance was measured using precision, recall, F1 score, and accuracy metrics to identify any potential overfitting of the data efficiently.