Machine-learning-based model for analysing and accurately predicting factors related to burnout in healthcare workers.

Objective: The aim of this study is to analyse the factors affecting medical burnout in hospitals, identify the characteristics of staff experiencing high levels of burnout and devise a practical and sustainable prediction mechanism. Methods: A survey was conducted to access the current situation, followed by a regression analysis using data from the Maslach Burnout Inventory General Survey, demographic information related to healthcare personnel and employee job satisfaction metrics from the hospitals under study. Subsequently, four predictive models-logistic regression, K-nearest neighbour, decision tree and random forest (RF)-were employed to predict the degree of healthcare burnout. Results: The investigation revealed that 61.2% of the medical staff in the hospitals under study exhibited at least one symptom of burnout, with 9.8% experiencing high levels of burnout. Elevated rates of high burnout were observed in the 30-39 age group, among physicians and surgeons, and among those with 0-5 years of experience. In terms of predictive methods, the RF model demonstrated suitability for predicting burnout among medical staff, achieving a prediction accuracy of approximately 80%. Conclusions: A significant correlation was found between job satisfaction and burnout levels. Physicians and surgeons with less than a decade of professional experience are more prone to high levels of burnout. The RF model proved effective for predicting the burnout level among medical staff, consistently achieving an accuracy rate close to 80%. These findings can serve as valuable insights for hospital administrators in their effort to prevent and mitigate burnout among medical staff.