  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.
