Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment
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 0.9
Resource information
- Authors
- Tang, Hao, Rekavandi, Aref Miri, Rooprai, Dharjinder, Dwivedi, Girish, Sanfilippo, Frank, Boussaid, Farid, Bennamoun, Mohammed
- Research area
- Artificial intelligence
- Research topics
- Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Computers and Society (cs.CY), Neurons and Cognition (q-bio.NC), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Biological sciences, FOS: Biological sciences
- Publication date
- 2023
- DOI
- 10.48550/arxiv.2303.06052
- Upstream last modified
- Mar 12, 2023
- License
- Creative Commons Attribution Non Commercial Share Alike 4.0 International
- Last record update
- Nov 4, 2026
About this resource
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.