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hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm

Maryam Tayefi1, Mohammad Tajfard2, Sara Saffar3

  • 1Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, 99199-91766 Mashhad, Iran ; Department of New Sciences and Technologies, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Computer Methods and Programs in Biomedicine
|March 1, 2017
PubMed

Insights

A decision tree model accurately predicts coronary heart disease (CHD) using clinical biomarkers and risk factors. High-sensitivity C-reactive protein (hs-CRP), fasting blood glucose (FBG), gender, and age were key predictors in this data mining approach.

Area of Science:

  • Cardiology
  • Data Mining
  • Public Health

Background:

  • Coronary heart disease (CHD) poses a significant global health challenge.
  • Predictive algorithms integrating clinical biomarkers and traditional risk factors aid CHD detection and intervention.
  • Decision tree (DT) models offer a data mining approach for uncovering hidden patterns in large datasets.

Purpose of the Study:

  • To develop a predictive model for coronary heart disease (CHD) utilizing a decision tree (DT) algorithm.
  • To identify key risk factors associated with CHD using data mining techniques.

Main Methods:

  • A dataset of 2346 individuals (1159 healthy, 1187 with coronary angiography) was analyzed.
  • Ten variables including age, sex, FBG, TG, hs-CRP, TC, HDL, LDL, SBP, and DBP were input into the DT algorithm.

Main Results:

  • The DT model achieved high performance metrics: 96% sensitivity, 87% specificity, and 94% accuracy in identifying CHD.
  • Serum hs-CRP levels emerged as the most significant predictor, followed by FBG, gender, and age.

Conclusions:

  • The developed DT model demonstrates high accuracy, specificity, and sensitivity for CHD prediction.
  • Further validation in prospective studies is recommended to confirm the model's clinical utility.
Abstract

Keywords:
Coronary artery diseaseData miningDecision treehs-CRP

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