Unlocking Minds: An Adaptive Machine Learning Approach for Early Detection of Depression
Journal Title: Healthcraft Frontiers - Year 2024, Vol 2, Issue 2
Abstract
Depression, a prevalent and severe medical condition, significantly impairs emotional well-being, cognitive functions, and behavior, often leading to substantial challenges in daily functioning and, in severe cases, an increased risk of suicide. Affecting approximately 264 million individuals worldwide across diverse age groups, depression necessitates effective and timely detection for intervention. In primary healthcare, the Patient Health Questionnaire-9 (PHQ-9) serves as a crucial tool for screening depression. This study leverages the PHQ-9 dataset, comprising 12 features and 534 samples, to evaluate depression levels using advanced machine learning (ML) techniques. A comparative analysis of the Support Vector Classifier (SVC) and AdaBoost Classifier (ABC) was conducted to determine their efficacy in classifying depression severity on a scale from 0 to 4. The SVC emerged as the superior model, achieving an accuracy of 94%. This research contributes to the early detection and prevention of depression by proposing an interactive interface designed to enhance user engagement. Future work will focus on expanding the dataset to improve model generalization and robustness, thereby facilitating more accurate and widespread applications in clinical settings.
Authors and Affiliations
Hafiz Burhan Ul Haq, Muhammad Nauman Irshad, Muhammad Daniyal Baig
Keywords
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- EP ID EP744657
- DOI https://doi.org/10.56578/hf020205
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