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The application of a decision tree to establish the parameters associated with hypertension

Maryam Tayefi1, Habibollah Esmaeili2, Maryam Saberi Karimian3

  • 1Biochemistry and Nutrition Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Computer Methods and Programs in Biomedicine
|February 12, 2017
PubMed

Insights

This study used a decision-tree algorithm to identify hypertension risk factors in over 9000 individuals. The model accurately identified key factors, aiding in developing hypertension management programs.

Area of Science:

  • Cardiovascular Health
  • Data Mining in Medicine
  • Public Health Research

Background:

  • Hypertension is a significant risk factor for cardiovascular disease (CVD).
  • Identifying hypertension risk factors is crucial for effective management and prevention strategies.
  • Data mining offers advanced methods for analyzing complex health datasets.

Purpose of the Study:

  • To establish factors associated with hypertension using a decision-tree algorithm.
  • To develop a predictive model for identifying individuals at risk of hypertension.
  • To inform the development of targeted hypertension management programs.

Main Methods:

  • A cross-sectional study involving 9078 subjects.
  • A decision-tree algorithm was employed as a supervised classification method.
  • Two models were evaluated using training (70%) and testing (30%) datasets, with ROC curve analysis for validation.

Main Results:

  • The prevalence of hypertension in the study population was 32%.
  • Decision-tree model I achieved 73% accuracy, 63% sensitivity, 77% specificity, and an AUC of 0.72.
  • Decision-tree model II achieved 70% accuracy, 61% sensitivity, 74% specificity, and an AUC of 0.68.

Conclusions:

  • A decision-tree model was successfully developed to identify hypertension risk factors.
  • The model can be utilized for developing effective hypertension management strategies.
  • This data mining approach provides valuable insights into hypertension etiology and prevention.
Abstract

Keywords:
Data miningDecision treeHypertension

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