To establish and validate the model of forecasting acute myocardial infarction in men.

From January to June 2016, 205 male patients admitted to the department of cardiology of the People′s Hospital of Wuhan University with angina pectoris or acute myocardial infarction were included in our study. Among them, 151 patients served as training set and 54 patients served as validation set. Random forest was used to rank the importance of predicting acute myocardial infarction. According to the OOB error, AIC and BIC criterion, the sorting indexs were screened and the prediction model was constructed. Multidimensional scaling (MDS) was used to observe the ability of the model to differentiate acute myocardial infarction from angina pectoris, and validation set data was used to investigate whether the random forest could distinguish between acute myocardial infarction and angina pectoris.

The 19 indicators were ranked according to mean Decrease Accuracy and mean Decrease Gini index. C-reactive protein, neutrophil absolute value and blood sugar inclusion model were screened by OOB error, AIC criterion and BIC criterion. In external validation, 26 of 31(83.87%) patients with acute myocardial infarction were correctly identified, and 19 of 23(82.61%) patients with angina pectoris were correctly identified.

Random forest-based predictive model can well distinguish between acute myocardial infarction and angina pectoris.