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 | A Survey on Early Warning System for People Burnout using Machine Learning |
|---|---|
| ABSTRACT | Burnout has become a significant concern in modern academic and professional environments due to increased workload, prolonged screen exposure, and high-performance expectations. Early identification of burnout symptoms is essential to prevent severe mental health consequences such as anxiety, depression, and reduced productivity. An early warning system for burnout aims to detect stress patterns and risk factors before burnout aims to detect stress patterns and risk factors before burnout reaches a critical stage. This survey paper presents an overview of machine learning-based burnout detection and early warning systems. It focuses on the use of data-driven approaches such as behavioral analysis, questionnaire-based assessment, physiological signals, and usage patterns to predict burnout levels. Machine learning algorithms such as decision trees, support vector machines, and neural networks have shown promising results in identifying burnout indications. This paper reviews existing research, commonly used algorithms, system architecture, application, and challenges associated with burnout prediction systems. The survey is intended to serve as foundation for a future project that aims to design and implement an intelligent burnout early warning system using machine learning techniques. |
| AUTHOR | OM JADHAV, MANASI KATKAR, PRAVIN GAIKWAD, MASOOD MADKI, AMRITA SHIRODE Diploma Students, Department of Artificial Intelligence & Machine Learning, AISSMS Polytechnic, Pune, India Guide, Department of Artificial Intelligence & Machine Learning, AISSMS Polytechnic, Pune, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401084 |
| pdf/84_A Survey on Early Warning System for People Burnout using Machine Learning.pdf | |
| KEYWORDS | |
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