Heart Disease Prediction using Graph Neural Network

Authors

  • Rakhi Wajgi Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra (India)
  • Tushar Champaneria Department of Computer Engineering, Governemnt Engineering College, Modasa, Gujarat,(India)
  • Dipak Wajgi Department of Computer Engineering, St. VincntPallotti College of Engineering and Technology, Maharashtra (India)
  • Yogesh Suryawanshi Department of Electronics Engineering, YeshwantraoChavan College of Engineering , Nagpur, INDIA
  • Dinesh Bhoyar Department of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, INDIA
  • Ajinkya Nilawar Department of Electronics and Communication Engineering, Shri Ramdeobaba College of Engineering and Management, Maharashtra (India)

Keywords:

Cardiovascular disease, Graph Neural Network, Optimizer

Abstract

Heart is an important organ playing vital role in the life of living organisms. Heart and circulatory disease encompasses a range of conditions affecting the heart and blood vessels, including coronary artery disease, arrhythmias, and heart failure mechanism. Early detection of malfunctioning before failure of heart is necessary. This paper deals with the model built using Graph Neural Network (GNN) to predict heart disease so that mortality rate caused due to sudden heart failure can be reduced. In order to improve the accuracy of GNN-based model, different optimizers are used. They are help to optimize or improve the model's performance by iteratively updating its parameters to reach the optimal values that minimize the difference between predicted and actual outputs. The proposed model is applied on a real dataset from kaggle containing 14 features. Out of all optimizers, RMSprop outperforms otherswith accuracy of 91% and MSE of 48%.

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Published

12.01.2024

How to Cite

Wajgi, R. ., Champaneria, T. ., Wajgi, D. ., Suryawanshi, Y. ., Bhoyar, D. ., & Nilawar, A. . (2024). Heart Disease Prediction using Graph Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 280–287. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4514

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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