2023/03/09 by Hao Tang, Tang, Hao, Aref Miri Rekavandi +11 · 1 citation
Mathematics · Medicine · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Boosting (machine learning) #Computer science #Computers and Society (cs.CY) #Decision tree #Environmental health #FOS: Biological sciences #FOS: Computer and information sciences #Gradient boosting #Machine Learning (cs.LG) #Machine learning #Mathematics #Medicine #Mental Health via Writing #Neurons and Cognition (q-bio.NC) #Poison control #Psychology #Random forest #Rank (graph theory) #Risk analysis (engineering) #Statistics #Suicide Risk #Suicide prevention
paper · pdf · doi:10.48550/arxiv.2303.06052
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviours. Data augmentation techniques and ML models are utilized to predict the associated risk. Furthermore, SHapley Additive exPlanations (SHAP) and correlation analysis are used to rank the importance of variables in predictions. Experimental results indicate that Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models achieve the best results while DT has the best performance with an accuracy of 95:23% and an Area Under Curve (AUC) of 0.95. As per SHAP results, anger problems, depression, and social isolation are the leading variables in predicting the risk of suicide, and patients with good incomes, respected occupations, and university education have the least risk. Results demonstrate the effectiveness of machine learning and XAI framework for suicide risk prediction, and they can assist psychiatrists in understanding complex human behaviours and can also assist in reliable clinical decision-making.