An Optimized Depression Detection Technique Using Behavioral Analysis and Machine Learning

In recent years, the increasing prevalence of depression has prompted the need for effective detection techniques. This study presents an optimized depression detection technique that leverages behavioral analysis and Machine Learning (ML) to identify depressive symptoms accurately. By analyzing various behavioral indicators such as facial expression, speech characteristics, and activity levels, the proposed methodology extracts significant features that contribute to depression assessment. A robust ML framework is employed, utilizing algorithms such as KNN, Random Forests, and Ensemble methods, to classify individuals based on their behavioral data. The experimental outcome was determined by many performance matrices, including F1_score, recall, accuracy, and precision. The experimental result shows that the proposed model achieved a higher accuracy of 98.3%, precision of 98.11%, recall of 98.16, and F1 score of 98.27. This research not only advances the field of mental health diagnostics but also paves the way for developing more personalized and timely interventions for individuals at risk of depression.