An Explainable Triple-Layered Ensemble Model for Early Prediction of Suicide Risk Using Machine Learning

Abstract

Suicide ranks as the 18th leading cause of death worldwide among young adults, claiming over 720,000 lives each year. Early detection of individuals at risk is essential for timely intervention. This study introduces a Triple-Layer Ensemble (TLE) model that predicts suicidal behavior using machine learning techniques. The proposed model combines Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) to enhance prediction accuracy. Experimental results show that the TLE model surpasses individual classifiers and traditional ensemble methods, achieving 94.81% accuracy, 98.15% ROC-AUC, and a Matthews Correlation Coefficient (MCC) of 92.23%. To improve interpretability, Explainable AI (XAI) methods, Local Interpretable Model-Agnostic Explanations (LIME), and Shapley Additive Explanations (SHAP) highlight key predictors such as mental support, stress levels, and self-harm history. Additionally, a web-based platform incorporating the TLE model provides real-time suicide risk assessment, enabling healthcare professionals to implement personalized interventions. The proposed framework delivers high predictive performance with transparency and interpretability, offering a scalable solution for early suicide risk prediction and prevention.