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 | Harnessing Machine Learning for Advanced Mental Health Analytics and Well-Being Optimization |
|---|---|
| ABSTRACT | : The prevalence of Stress and other mental health concerns, anxiety, depression, and emotional fatigue is rising, underscoring the need for proactive, scalable, and intelligent support systems. Subjective self-reporting and rare professional examinations are common limitations of traditional mental health assessment approaches, which prevent prompt action and ongoing monitoring. By allowing data-driven insights, early risk identification, and individualized assistance, using machine learning for enhanced mental health analytics and well-being optimization presents a viable option. Self-reported mood evaluations, textual and speech-based emotional expressions, behavioral patterns, and physiological signals from wearables and digital devices are just a few of the multimodal data sources that this method incorporates. These many data streams are examined by artificial learning techniques including natural language processing, time-series analysis, and predictive modeling to find significant trends pertaining to mental health. The retrieved insights are utilized to monitor changes over time, identify possible dangers, and evaluate mental health statuses. |
| AUTHOR | ABHILASH H M, NASEERHUSEN ANKALAGI PG Student, Dept. of MCA, City Engineering College, Bengaluru, India Assistant Professor, Dept. of MCA, City Engineering College, Bengaluru, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312057 |
| pdf/57_Harnessing Machine Learning for Advanced Mental Health Analytics and Well-Being Optimization.pdf | |
| KEYWORDS | |
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