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@article{174071,
        author = {Sure Sai Venkata Jaideep and K. Hari Veerraju and Kandipalli Sandeep Kiran and Meruga Sri Vineesh and Billa Surya},
        title = {Leveraging Machine Learning to Predict Depression},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3183-3189},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174071},
        abstract = {Depression, clinically recognized as Major Depressive Disorder (MDD), is a debilitating mental health condition marked by pervasive sadness, anhedonia, and impaired daily functioning. Timely detection is paramount to facilitating early intervention, mitigating relapse, and alleviating the profound emotional and financial burdens associated with the disorder. This study deploys cutting-edge machine learning methodologies to advance the early detection of depression through a robust web-based screening platform. By harnessing state-of-the-art algorithms including Support Vector Machines, Random Forests, and Neural Networks the system meticulously analyzes user responses from a validated depression assessment. Empirical evaluations demonstrate exceptional diagnostic accuracy, underscoring the system's capacity to revolutionize mental health screening by delivering rapid, scalable, and reliable evaluations. This research exemplifies the transformative potential of ML-driven diagnostic tools in enhancing clinical decision-making and fostering proactive mental health management.},
        keywords = {Depression, Machine Learning, Early Detection, Web-Based Screening, Major Depressive Disorder (MDD), Diagnostic Algorithms},
        month = {March},
        }