Comparative Analysis for Prediction of Coronary Artery Disease Using Machine Learning Algorithms

Authors

  • Ravi Hosur Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Pavan Mahendrakar Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Mahesh Nagaral Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Anand Hiremath Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India
  • Shashidhar B. Patil Department of ComputerApplications (MCA), BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, Vijayapur586103, Karnataka, India

Keywords:

Coronary Artery Disease (CAD), Naïve Bayes, Convolution Neural Network (CNN), Logistic Regression, Machine Learning, Generative Adversarial Networks, K-Nearest Neighbor (KNN)

Abstract

Cardiovascular disease, another name for heart disease, is linked to a number of conditions that affect the heart. Over the past few decades, heart disease has consistently been the leading cause of death. Numerous risk factors for heart disease are also identified, as well as the need of early disease management. This study includes a number of heart disease-related characteristics as well as models based on machine learning techniques like Nave Bayes, Convolution Neural Networks, and Logistic Regression. All previous trials relate to utilising a subset of 14, but we used the publicly accessible UCI heart disease database, which has 76 features. The purpose of this study is to estimate a patient's risk of getting heart disease. We have applied three machine learning classifiers for comparative analysis. In comparison to CNN and the Naive Bayesian algorithm, Logistic Regression has a higher accuracy of 93.22 percent.

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Published

12.01.2024

How to Cite

Hosur, R. ., Mahendrakar, P. ., Nagaral, M. ., Hiremath, A. ., & Patil, S. B. . (2024). Comparative Analysis for Prediction of Coronary Artery Disease Using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 735 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4559

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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