Transactions on Machine Intelligence

Transactions on Machine Intelligence

Cardiovascular Disease Diagnosis Using the Combination of Principal Component Analysis Algorithm and Regression Tree

Document Type : Original Article

Authors
H. R. Aviny 1
M. Ghasemi 2
M. Fazlazad 3
1 Ph.D. student, Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj branch, Islamic Azad University, Yasouj, Iran
2 Masters student, Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj branch, Islamic Azad University, Yasouj, Iran
3 Masters, Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj branch, Islamic Azad University, Yasouj, Iran
10.47176/TMI.2023.114
Abstract
Cardiovascular disease stands as a prominent global cause of mortality, emphasizing the pivotal need for effective diagnostic and treatment strategies. Recognizing the significance of early detection, this study centers on employing the regression tree algorithm as a primary method. To gauge the precision of cardiovascular disease diagnosis, we scrutinized a dataset encompassing 270 patient samples and 14 distinct characteristics. The implementation approach involved a dual deployment of the Principal Component Analysis (PCA) algorithm and the regression tree algorithm. Employing PCA, we streamlined the feature set from 14 to 8, followed by the application of the regression tree algorithm to enhance detection accuracy. The decision tree classification method adopted encompasses critical facets such as feature selection, tree generation, and pruning. Implementation of these procedures was facilitated through the Weka tool, a data mining software. The collaborative utilization of PCA and the regression tree algorithm culminated in a noteworthy improvement, yielding a diagnostic accuracy increase of 81.48% in detecting cardiovascular disease.
Keywords
Cardiovascular Disease
Principal Component Analysis Algorithm
Regression Tree Algorithm
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Transactions on Machine Intelligence
Volume 6, Issue 2
Spring 2023
Pages 114-125

  • Receive Date 10 April 2023
  • Revise Date 20 May 2023
  • Accept Date 26 June 2023