Abstract: OBJECTIVE To construct risk prediction models of nosocomial infection in intensive care unit by using Chi-Square Automatic Interaction Detection (CHAID) algorithm and the Logistic regression analysis, and compare the quality of the prediction results. METHODS Hospitalized patients admitted to Intensive Care Unit (ICU, RICU, CCU/CNICU) for more than 48 h and transferred out of Intensive Care Unit for less than 48 h from Jan. 2016 to Jun. 2017 were included in this retrospective study. Risk prediction models of nosocomial infection were constructed by using CHAID algorithm and the Logistic regression analysis. The risk models were evaluated by conducting goodness-of-fit tests. The area under ROC curve analysis was used to compare the two prediction models. RESULTS Among the 1232 patients collected, 182 cases of nosocomial infections occurred, and the infection rate was 14.8%. Classification tree model and Logistic regression showed that hospitalization time greater than 10 days, APACHE Ⅱ score greater than or equal to 20 points, and more than 7 days of central venous intubationwere the most important influencing factors for nosocomial infections. The Risk statistics (0.286) revealed that CHAID model fitted the data well. The sensitivity, specificity and area under the ROC curve (AUC) of the CHAID model were 83.5%, 59.3% and 0.788 (95% CI 0.742~0.835), respectively. The sensitivity, specificity and AUC of the Logistic regression model were 80.2%, 81.3% and 0.869 (95% CI 0.832~0.906), respectively. There were significant differences in results of the CHAID model and the Logistic regression model (Z=4.656, P＜0.001). CONCLUSION The Logistic regression model is superior to the CHAID model in predicting nosocomial infections. The combination of the two models enable us to find out risk factors of nosocomial infection at different levels, which can provide a reference for further prevention and control measures of nosocomial infections.