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

ABSTRACT:

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

SUBMITTER: Liu C 

PROVIDER: S-EPMC12414203 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature