Objective To construct prediction models for the clinical outcomes of coronavirus disease 2019 (COVID-19) patients using machine learning algorithms, and explore the outcome-related factors. Methods The clinical indexes and outcomes (in-hospital mortality and receiving tracheal intubation) of COVID-19 patients who were admitted to Wuhan Huoshenshan Hospital or Guanggu Branch of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology from Feb. 5 to Apr. 15, 2020 were collected. The prediction models for the clinical outcomes were constructed using artificial neural network (ANN), naive Bayes, logistic regression and random forest algorithms. Results A total of 4 804 COVID-19 patients were included, of whom 100 (2.08%) patients died and 87 (1.81%) patients received tracheal intubation during the hospitalization. White blood cell (WBC), albumin, calcium, blood urea nitrogen, creatine kinase-myocardial band (CK-MB) and age were the most correlated variables with in-hospital mortality. WBC, lymphocyte, hypersensitivity C reaction protein (hs-CRP), total bilirubin, calcium and age were the most correlated variables with in-hospital tracheal intubation. With the above variables and based on the 4 machine learning algorithms, the prediction models for in-hospital mortality and tracheal intubation were constructed. In the 4 prediction models, the model constructed based on naive Bayes algorithm had the best performance in predicting in-hospital mortality (area under curve[AUC]=0.952, 95% confidence interval[CI] 0.925-0.979) and tracheal intubation (AUC=0.948, 95% CI 0.896-0.965) versus the models constructed based on ANN, logistic regression and random forest algorithms (the AUC[95% CI] values for predicting in-hospital mortality were 0.938[0.882-0.993], 0.926[0.865-0.987] and 0.867[0.780-0.954], and the AUC[95% CI] values for predicting in-hospital tracheal intubation were 0.932[0.814-0.980], 0.935[0.817-0.981] and 0.936[0.921-0.972], respectively). Conclusion The 4 machine learning algorithms have good performance in predicting the clinical outcomes of COVID-19 patients. WBC, albumin, calcium, blood urea nitrogen, CK-MB and age can be used to predict the in-hospital mortality of COVID-19 patients; while WBC, lymphocyte count, hs-CRP, total bilirubin, calcium and age can be used to predict the in-hospital tracheal intubation.