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.

翻译：本研究探讨了可解释人工智能（XAI）技术在预测自杀风险及识别此类行为主导原因方面的有效性。采用数据增强技术与机器学习模型进行相关风险预测，并利用SHAP（SHapley Additive exPlanations）及相关性分析对预测中变量的重要性进行排序。实验结果表明，决策树（DT）、随机森林（RF）及极端梯度提升（XGBoost）模型取得了最佳效果，其中决策树性能最优，准确率达95.23%，AUC（曲线下面积）为0.95。根据SHAP结果，愤怒问题、抑郁和社会孤立是预测自杀风险的主要变量，而收入良好、职业受尊重及受过大学教育的患者风险最低。结果证明了机器学习与XAI框架在自杀风险预测中的有效性，可辅助精神科医生理解复杂人类行为，并有助于可靠的临床决策。