An Enhanced Deep Learning Framework Integrating Adaptive Whale Optimization and Natural Language Processing for Mental Health Disorder Diagnosis

Abstract

Mental health disorders are among the biggest global public health challenges, affecting over 1 billion people worldwide and placing a burden on healthcare systems, economies, and societies. Despite progress in clinical psychiatry, mental health professionals still rely heavily on subjective judgments to make diagnoses, leading to delays and inconsistency in diagnosis-making and making early intervention difficult. The evolution of technology-enabled communication channels, i.e., social media, has enabled researchers to employ computational techniques to study digital signatures and patterns and identify psychological distress or behavioral changes in people via their electronic interactions. This study proposes a psychiatric disorder classification system based on Natural Language Processing (NLP) and an Enhanced Deep Learning model powered by the Adaptive Whale Optimization Algorithm (EDL-AWOA). It examines how users in Reddit mental health communities communicate textual data to assist a machine learning algorithm in diagnosing depression and anxiety. It also aims to build a diagnostic support tool using these findings to help identify early signs of mental illness. The results integrate existing measures into a system designed to complement clinical judgment, with the hope that clinicians will adopt these additional steps more quickly. When evaluated with standard metrics of classification accuracy, precision, recall, F1 score, and the confusion matrix, the strong results in the training phase are achieved, including 93.99% accuracy | 94.01% precision | 93.99% recall | 93.99% F1 Score.