Performance Analysis of Explainable AI (XAI) based Machine Learning Algorithms for Mental Health Assessment

Abstract

Mental health disorders such as anxiety, depression, bipolar disorder, personality disorders and stress have become significant public health concerns. This paper proposes a comparative analysis of machine learning models based on Explainable Artificial Intelligence (XAI) for mental health diagnostics using social media data from Reddit and Twitter. Two publicly available mental health related datasets were preprocessed using tokenization, stopwords removal, removal of special characters, removal of URLs, and TF-IDF vectorization. Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest classifiers were analyzed before and after XAI-based LIME technique was applied. LIME technique helped to determine influential features and increase transparency of the models using feature selection. From experimental results, it can be concluded that Logistic Regression provided the best accuracy (76.84%) among all classifiers on the largest dataset after applying LIME-based feature refinement, while Decision Tree and Random Forest provided 81.94% accuracy on the second dataset using positive features.