Evaluating the Performance, Accuracy, and Reliability of a Computational Model for Automated Document Classification in Mental Health Diagnosis

Abstract

The increasing prevalence of mental health disorders demands innovative computational tools to assist clinicians in diagnosis and monitoring. This study evaluates the performance, accuracy, and reliability of a proposed document classification framework for mental health diagnosis based on machine learning and natural language processing (NLP). Using a benchmark dataset of mental health reports, the study assesses the model across key metrics accuracy, recall, and precision and identifies limitations for future enhancement. Results indicate that Support Vector Machine (SVM) and Neural Network models outperform conventional classifiers such as Naïve Bayes and Random Forest in terms of diagnostic precision and generalization capability. Recommendations for improving dataset diversity, feature extraction, and interpretability are proposed.