Objective:To establish a model for predicting the mental health status of nurses based on machine learning algorithm.Methods:In February 2020, the nurses from Shantou Central Hospital and Cancer Hospital of Shantou University Medical College were recruited by convenience sampling, investigated using the Self-reporting Inventory, Coping Style Questionnaire, Social Support Rating Scale and Work Attitude Scale. Mental health status was treated as a dichotomous variable, and candidate predictors were screened out by univariate and multivariate Logistic regression analysis. The subjects were randomly divided into a training set (80%) and a test set (20%). Then five prediction models of nursing staff mental health status were constructed using the five machine learning methods (Logistic Regression, Artificial Neural Network, C5.0 Decision Tree, Bayesian Network and Support Vector Machine), verified and compared to screen out the model with the highest predictive efficiency.Results:A total of 415 nurses were enrolled, and the positive detection rate of mental health symptoms was 20.48%. According to univariate and multiple Logistic regression analysis, candidate predictors were work attitude ( OR=1.098, 95% CI 1.028-1.174), self-accusation ( OR=7.703, 95% CI 2.014-29.468), problem-solving( OR=0.131, 95% CI 0.025-0.686), the number of night shifts per month ( OR=0.204, 95% CI 0.073-0.573)and support availability ( OR=0.830, 95% CI 0.701-0.984). The accuracy of prediction of Logistic Regression, Artificial Neural Network, C5.0 Decision Tree, Bayesian Network and Support Vector Machine were 84.21%, 85.53%, 82.89%, 78.95%, 84.21%. The area under the ROC curve was 0.801, 0.825, 0.777, 0.583, 0.774. Artificial Neural Network was significantly more effective than Logistic regression, C5.0 Decision Tree, Bayesian Network and Support Vector Machine (DeLong test, P<0.05). Conclusions:The machine learning based predictive models for nursing staff mental health status has higher predictive value, which can be applied into nursing staff mental health screening decisions to accurately grasp its dynamic changes, early identification of high-risk mental health abnormalities and early intervention. Work attitude, self-accusation, problem-solving, the number of night shifts per month and support availability was predictors to construct predictive models.