Coronary Heart Disease Prediction Using GKFCM with RNN

For humans, coronary heart disease is the number one killer. A coronary angiogram is the most common method for identifying coronary heart disease. The patient risks experiencing a bodily injury, problems, and bad reactions during this procedure. Coronary angiography has a hefty price tag. However, the heart color Doppler echocardiography report, biochemical blood markers, and other essential data may reveal the amount of heart damage in a patient. This article proposed for predicting coronary heart disease using Gaussian Fuzzy C-Means Clustering (GKFCM) with Recurrent Neural Network (RNN). This study predicts coronary heart disease based on datasets. The datasets are normalized then the dataset has clustered using GKFCM and heart disease prediction using hybrid method as GKFCM with RNN. The Experimental results show the proposed model has achieved the highest accuracy at 99%.