Skip to content
Research Article Open access CC BY 3.0

Testing the Probability of Heart Disease Using Classification and Regression Tree Model

Mohammad Subhi Al-batah

Annual Research & Review in Biology · pp. 1713–1725 · Published 12 Feb 2014

10.9734/ARRB/2014/7786

Abstract

The objective of this study is to predict the presence of heart disease with reduced number of attributes using data mining techniques. The term heart disease is related to all diverse diseases affecting the heart. The exposure of heart disease from various factors is an issue which is not free from false presumptions often accompanied by unpredictable effects. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. In our work, Classification and Regression Tree (CRT) is proposed to determine the attributes which contribute more towards the diagnosis of heart ailments, which indirectly may reduce the number of tests which are needed to be taken by a patient. The dataset used consists of 270 cases. Originally, thirteen attributes were involved (age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate, exercise induced angina, depression induced by exercise relative to rest, slope, number of vessels colored by fluoroscopy, and exercise thallium scintigraphic defects). To evaluate the performance of the model the sensitivity, specificity, and accuracy are calculated. Comparison with other data mining techniques is presented. The simulation result obtained from the model enables to establish significant patterns and relationships between the medical factors and heart disease.

Modeling Testing Decision tree Heart disease Features reduction Classification and regression tree.

References (32)

  1. 1 UCI Repository of machine learning databases
  2. 2 Prediction of Coronary Heart Disease Using Risk Factor Categories [DOI]
  3. 3 Data Mining
  4. 4 Principles of Data Mining
  5. 5 Intelligent heart disease prediction system using data mining techniques [DOI]
  6. 6 SPRINT: A Scalable Parallel Classifier for Data Mining
  7. 7 SLIQ: A fast scalable classifier for data mining [DOI]
  8. 8 Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction [DOI]
  9. 9 International application of a new probability algorithm for the diagnosis of coronary artery disease [DOI]
  10. 10 Experiments in induction
  11. 11 Decision tree regression for soft classification of remote sensing data [DOI]
  12. 12 Empirical Study on Applications of Data Mining Techniques in Healthcare [DOI]
  13. 13 Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees [DOI]
  14. 14 Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients [DOI]
  15. 15 Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks [DOI]
  16. 16 Diversity in Neural Network Ensembles
  17. 17 A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL) [DOI]
  18. 18 Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing [DOI]
  19. 19 Comparing association rules and decision trees for disease prediction [DOI]
  20. 20 The Medical Applications of Attribute Weighted Artificial Immune System (AWAIS): Diagnosis of Heart and Diabetes Diseases [DOI]
  21. 21 Risk functions for prediction of cardiovascular disease in elderly Australians: the Dubbo Study [DOI]
  22. 22 Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems [DOI]
  23. 23 Predict the Diagnosis of Heart Disease Patients Using Classification Mining Techniques [DOI]
  24. 24 Identification of ischemic heart disease via machine learning analysis on magnetocardiograms [DOI]
  25. 25 Modeling and design of evolutionary neural network for heart disease detection
  26. 26 Data mining of magnetocardiograms for prediction of ischemic heart disease [DOI]
  27. 27 Data Modelling and Specific Rule Generation via Data Mining Techniques
  28. 28 How well can we predict coronary heart disease? Findings in the United Kingdom Heart Disease Prevention Project. [DOI]
  29. 29 A Heart Disease Prediction Model using Decision Tree [DOI]
  30. 30 Cardiovascular Disease Risk Factors among Adult Australian-Lebanese in Melbourne [DOI]
  31. 31 Data mining: concepts and techniques [DOI]
  32. 32 Unanimous Voting using Support Vector Machines

Cited by 46

ISO 14000 as a driver of sustainability: Exploring its alternative drivers beyond ISO 9000 standards

Heba F. Zaher, Santi Setyaningsih · Journal of Infrastructure Policy and Development · 2024

Enhancement over DBSCAN Satellite Spatial Data Clustering

M. Al-batah, Enas Rezeg Al-Kwaldeh, Mutaz Abdel Wahed · Journal of Electrical and Computer Engineering · 2024

Data-Driven Heart Disease Risk Prediction with Machine Learning on Healthcare Datasets

Sakchai Tangprasert, Ravipa Sonthana, Yuenyong Nilsiam · 2023 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C) · 2023

A Machine Learning Approach to Evaluate Variables of Math Anxiety in STEM Students

D. Soysal, M. Bani-Yaghoub, T. Riggers-Piehl · Pedagogical Research · 2022

Comparison of Coronary Heart Disease Prediction models using various Machine Learning Algorithms

Sunil Tiwari, S. K. Garg · Journal of Engineering Research · 2021

My resumé

K. Kozlowski · Acta medicorum polonorum · 2020

The profile of heart attack symptoms of cardiovascular patients at XYZ hospital

R. Setiadi, Franky · IOP Conference Series: Materials Science and Engineering · 2019

Investigation of Naive Bayes Combined with Multilayer Perceptron for Arabic Sentiment Analysis and Opinion Mining

M. Al-batah, Shakir Mrayyen, M. Alzaqebah · Journal of Computer Science · 2018

Showing 31 of 46 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

119

Page views

0

PDF downloads

11

Outbound clicks

46

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.