Machine Learning-Driven Mental Health Detection: A Comparative Analysis of Existing Techniques and Development of a Predictive Computational Model

Abstract

Mental health diseases such as Depression, Anxiety, Post-Traumatic Stress Disorder (PTSD), attention deficit hyperactivity disorder (ADHD), bipolar disorder, etc., have become a major health issue worldwide. Limited access to clinical services and delayed diagnosis remain a challenge for early detection of many disorders. The fast expansion of social media platforms has led to large libraries of textual data generated by users, which can provide useful insight into psychological well-being. We propose a novel machine learning-driven predictive computational model based on DistilBERT for automated multi-class mental health identification from Reddit posts in this paper. The proposed framework comprises data preparation, tokenization, feature extraction, transformer-based contextual encoding and supervised classification. The experimental evaluation was performed on the Reddit Mental Health Dataset, which contains 13,727 samples and 2,746 testing samples. The proposed model has obtained an accuracy of 95.6%, a precision of 94.7%, a recall of 96.1% and an F1-score of 95.8%. Our results indicate that transformer-based models are robust and scalable solutions for real-world mental health testing and decision-support systems.