Computational Approaches for Emotional Burnout Detection: Machine Learning and Deep Learning Evaluation
Дата
2026
Науковий керівник
Назва журналу
Номер ISSN
Назва тому
Видавець
КПІ ім. Ігоря Сікорського
Анотація
This study focuses on the development and evaluation of computational techniques for classifying emotional burnout based on quantitative data analysis. Instead of relying on subjective psychometric questionnaires, we investigate the applicability of machine learning and deep learning algorithms to structured datasets. Several classical methods, including logistic regression, random forest, and gradient boosting, were systematically compared with a Deep Learning (DL) ensemble model. To enhance robustness, preprocessing steps such as feature selection, data balancing, and resampling were applied. The deep learning architecture, incorporating focal loss and adaptive threshold optimization, achieved the best performance. On 5-fold cross-validation, the proposed DL model obtained an overall accuracy of 86.3%, with precision of 0.815/0.887, recall of 0.786/0.904, and F1-scores of 0.800/0.895 for the negative and positive classes respectively. The results demonstrate that advanced computational models can provide scalable and generalizable tools for automatic detection tasks, forming a technical foundation for future integration into applied research and occupational health monitoring systems.
Опис
Ключові слова
emotional burnout, machine learning, deep learning, electroencephalography, maslach burnout inventory, feature selection, classification, mental health diagnostics, емоцiйне вигорання, машинне навчання, глибоке навчання, електроенцефалографiя, опитувальник вигорання Маслач, вiдбiр ознак, класифiкацiя, дiагностика психiчного здоров’я
Бібліографічний опис
Computational Approaches for Emotional Burnout Detection: Machine Learning and Deep Learning Evaluation / Mushta S. A., Mushta I. A., Popov A. O., Lysenko O. M., Tukaiev S. V. // Вісник НТУУ «КПІ». Радіотехніка, радіоапаратобудування : збірник наукових праць. – 2026. – Вип. 103. – С.69-77. – Бібліогр.: 27 назв.