Predicting Job Burnout Among Female Nurses in China With Machine Learning and Shapley Additive Explanations.

ABSTRACT: Job burnout among nurses is prevalent globally, particularly in China. However, few studies have been conducted on reliable tools for building predictive models. This cross-sectional study was conducted in four cities of Liaoning Province in China during the period from January to April 2022 by utilizing a self-administered smartphone questionnaire protocol, yielding 1400 responses from female nurses. We applied the least absolute shrinkage and selection operator (LASSO) and Boruta to identify the common predictors of job burnout. We then adopted and optimized three highly applicable machine learning (ML) algorithms-K-nearest neighbor (KNN), EXtreme Gradient Boosting (XGBoost), and random forest (RF)-to predict job burnout among female nurses. The values of area under curve (AUC) of KNN, R

SUBMITTER: Hu X 

PROVIDER: S-EPMC12745184 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature