International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Leveraging Machine Learning to Predict Depression |
|---|---|
| ABSTRACT | Depression is one of the most common mental health disorders worldwide and often remains undetected during its early stages due to limited accessibility to timely clinical assessment. This paper proposes an intelligent web-based depression prediction system that leverages Machine Learning techniques to assist in the early identification of depressive symptoms. The proposed platform employs a Random Forest classification model to analyze user responses collected through a structured psychological questionnaire and predicts the likelihood of depression based on behavioral, emotional, and lifestyle attributes. The system integrates secure user authentication, data preprocessing, feature extraction, prediction analysis, and result visualization within a unified web application. Experimental evaluation demonstrates reliable prediction performance with an intuitive user interface that enables rapid mental health assessment. The proposed solution provides an accessible, data-driven approach for early depression screening while supporting timely intervention and increasing awareness of mental health management. |
| AUTHOR | YANDRAPU DEEPAK, DR. T. V. S. SRIRAM PG Student, Dept. of MCA, Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT), Visakhapatnam, Andhra Pradesh, India Professor & HOD, Dept. of MCA, Nadimpalli Satyanarayana Raju Institute of Technology (NSRIT), Visakhapatnam, Andhra Pradesh, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407048 |
| pdf/48_Leveraging Machine Learning to Predict Depression.pdf | |
| KEYWORDS | |
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