Abstract: Objective To explore the value of gradient boosting machine (GBM) algorithms in predicting severe hand-foot-mouth disease (HFMD). Methods The medical data of children with HFMD admitted to Children's Hospital of Zhengzhou University from May to December in 2017 were collected and analyzed by the R software version 3.4.3. GBM and logistic regression model was built respectively and compared for the predictive performance. Results A total of 1 137 children with HFMD were enrolled, included 581 males and 556 females. The average age was 2.0±1.4 years. For GBM and logistic model, the prediction accuracy rates were 82.1%, 76.4% and the area under the ROC curves were 0.813 (95% CI, 0.796-0.829), and 0.752(95% CI, 0.693-0.731), respectively.In GBM analysis, the top three predictors were white blood cell count, EV71 and neutrophil ratio. The top three risk factors in the logistic model were blood glucose, EV71 and heart rate. Conclusion GBM model can be used to predict severe HFMD and it performs better than traditional logistic regression algorithm.