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[Application of Logistic regression and decision tree analysis in prediction of acute myocardial infarction events]

Sheng Zhang1, Zhenjie Hu2, Lu Ye3

  • 1Department of Neurology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China.

Zhejiang Da Xue Xue Bao. Yi Xue Ban = Journal of Zhejiang University. Medical Sciences
|January 20, 2020
PubMed

Insights

Decision tree models show comparable predictive value to logistic regression for acute myocardial infarction (AMI) events. These data mining methods are effective for AMI prevention and control.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Data Science

Background:

  • Acute myocardial infarction (AMI) poses a significant health challenge.
  • Accurate prediction of AMI events is crucial for timely intervention and improved patient outcomes.
  • Traditional statistical methods and emerging data mining techniques are being explored for predictive modeling.

Purpose of the Study:

  • To evaluate the application and predictive performance of decision tree methods compared to logistic regression for acute myocardial infarction (AMI) prediction.
  • To identify key clinical factors influencing AMI events using these modeling approaches.

Main Methods:

  • Retrospective analysis of clinical data from 295 patients undergoing coronary angiography.
  • Development of predictive models for AMI using logistic regression and decision tree algorithms (independent and dependent models).
  • Comparison of model performance using Receiver Operating Characteristic (ROC) curve analysis and Area Under the Curve (AUC) values.

Main Results:

  • Logistic regression identified history of coronary artery disease, multi-vessel coronary artery disease, statin use, and apolipoprotein A1 (ApoA1) level as independent predictors of AMI.
  • Decision tree models identified multi-vessel coronary artery disease, history of coronary artery disease, and ApoA1 level as key factors.
  • The logistic regression model achieved an AUC of 0.826, while decision tree models achieved AUCs of 0.765 and 0.726. The logistic regression model's AUC was significantly higher than the dependent decision tree model but comparable to the independent decision tree model.

Conclusions:

  • The logistic regression-independent decision tree model demonstrated comparable predictive value to the logistic regression model for AMI events.
  • Data mining techniques, such as decision trees, are feasible and effective tools for AMI prevention and control.
  • These findings support the integration of data mining in clinical practice for predicting and managing cardiovascular events.
Abstract

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