Document Type : Original Article
Authors
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
Abstract
Cardiovascular disease is a leading cause of death worldwide. Objective measures for diagnosing and treating this disease are crucial. Cardiovascular disease is a leading cause of death worldwide. Early detection is particularly significant. For this article, the primary method utilized is the regression tree algorithm. To assess the accuracy of cardiovascular disease diagnosis, we evaluated a dataset containing 270 patient samples and 14 characteristics. We employed a combination of the Principal Component Analysis algorithm and the regression tree algorithm for implementation. By using the Principal Component Analysis algorithm, we reduced the number of features from 14 to 8. Subsequently, we used the regression tree algorithm to achieve detection accuracy. This decision tree classification method comprises crucial aspects of feature selection, tree generation, and pruning. The implementation results were achieved with the utilization of the Weka tool, a data mining software. By employing the two algorithms in combination, our team successfully improved the accuracy of the diagnosis of cardiovascular disease by 81.48%.
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