Heart Disease Prediction using Graph Neural Network - Wajgi, Rakhi; Champaneria, Tushar; Wajgi, Dipak; Suryawanshi, Yogesh; Bhoyar, Dinesh; Nilawar, Ajinkya (International Journal of Intelligent Systems and Applications in Engineering, 2024)
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%.