Analysis of the Effect of Hidden Layer Units on Coronary Heart Prediction Using the Radial Basis Functions Algorithm

Abstract

Heart disease is a disease that occurs due to disturbances in the heart, especially when pumping blood so that it can cause death. Nearly half of deaths in the United States and other developed countries are caused by heart disease. Therefore, an early prognosis of heart disease is needed to prevent the risk of coronary heart disease. One thing that can be done is to predict coronary heart disease sufferers using the neural network method. This study conducted an analysis of the effect of hidden layer units on the neural network radial basis functions algorithm to predict coronary heart disease sufferers. This study obtained the highest accuracy at 10 hidden layers, namely 85.08%.