Identifying Critical Mental Health Indicators Using Ensemble and Explainable AI Techniques

Mental health issues have considerably increased in recent years. We need to devise innovative and effective means, such as ensemble models, for the timely and accurate diagnosis of depression. The models provide accurate predictions, but it is crucial to understand the model prediction behavior to ensure transparency and trust. This study uses ensemble techniques to predict depression and leverage Explainable Artificial Intelligence (XAI) techniques to address the black-box nature of these algorithms and enhance interpretability. A comprehensive publicly available depression dataset is used in this study, and the findings reveal that ensemble and explainable techniques provide a robust, reliable and transparent prediction. The study emphasizes the value of interpretability in AI-powered mental health applications, providing physicians with an open and reliable instrument for making decisions. The visualization techniques adapted in this article provide a comprehensive view towards the explainability of the model. The results will aid practitioners in distinguishing the contributing factors in mental health prediction, thereby improving trust in the classification models developed. The study summarizes that concentration and suicidal ideation are the most decisive factors and assist doctors in the accurate and timely prediction of the mental health of patients.