Predicting Mental Health Conditions from Text Using Interpretable Machine Learning

Abstract

Mental health disorders such as depression, anxiety, and post-traumatic stress disorder (PTSD) affect over one billion people worldwide, yet early detection remains a major clinical challenge. In recent years, text data from social media posts, clinical notes, and patient surveys has emerged as a rich source of signals for automated mental health screening. However, most existing machine learning models operate as black boxes, limiting clinical adoption. This paper presents an interpretable machine learning framework that combines natural language processing (NLP) feature extraction with explainable AI techniques — specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) — to predict mental health conditions from text while providing transparent, clinically meaningful explanations. A multi- class classification task involving depression, anxiety, PTSD, and healthy controls is performed on a dataset of 19,320 labelled text samples. The proposed XGBoost model with SHAP explanations achieves 87.3% accuracy and an AUC of 0.924, while the fine-tuned BERT model achieves 91.6% accuracy and an AUC of 0.961. Experimental results demonstrate that interpretability does not significantly compromise predictive performance, enabling trustworthy AI-assisted mental health screening.