Objective:Our study aims to predict acute kidney injury (AKI) in acute myocardial infarction (AMI) by establishing a random forest model.Methods:By using the clinical database from affiliated Dongyang Hospital of Wenzhou Medical University, a total of 1 363 AMI cases were included. Then, 75% of participants were analyzed as training subsets and the remaining 25% were testing subsets. The CARET package in R was used to filter variables and build random forest. The prediction ability of established model was evaluated by specificity, sensitivity, accuracy, relative operating characteristic curve (ROC curve) in testing subsets. In addition, the performance of random forest was compared with other 3 commonly used machine learning algorithms (Artificial Neural Network, Naive Bayes, and Support Vector Machine).Results:In this study, 30 variables including the demographic information, risk factors of cardiovascular disease, vital signs at admission, laboratory tests were identified and used to establish our random forest prediction model. The area under the curve of the testing subsets ROC was 0.893. The specificity and sensitivity of prediction model was 0.791 and 0.866, respectively. And the first creatinine, first blood urea nitrogen, and D-dimer after admission, age, mechanical ventilation were the top-five factors in this model. After comparing various machine learning algorithms, random forest model had a better performance.Conclusion:The random forest model would be used to predict the occurrence of AMI with AKI.