[Application of Logistic regression and decision tree analysis in prediction of acute myocardial infarction events].
摘要
OBJECTIVE: To evaluate the application of decision tree method and Logistic regression in the prediction of acute myocardial infarction (AMI) events. METHODS: The clinical data of 295 patients, who underwent coronary angiography due to angina or chest pain with unidentified causes in Zhejiang provincial People's Hospital during October 2018 and April 2019, were retrospectively analyzed. Fifty five patients were identified as AMI. Logistic regression and decision tree methods were performed to establish predictive models for the occurrence of AMI, respectively; and the models created by decision tree analysis were divided into Logistic regression-independent model (Tree 1) and Logistic regression-dependent model (Tree 2). The performance of Logistic regression and decision tree models were compared using the area under the receiver operating characteristic (ROC) curve. RESULTS: >0.05). CONCLUSIONS: The predictive value for AMI event was comparable between Logistic regression-independent decision tree model and Logistic regression model, implying the data mining methods are feasible and effective in AMI prevention and control.