Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network

Abstract

Heart disease is a deadly disease in human life. The mortality rate from any disease is the highest in the world. Therefore, before reaching the final stage of this heart disease, all precautionary measures must be taken. For this reason without the help of any kind of traditional methods, if we can scientifically diagnose heart disease at an early stage through various decision support systems, then surely death rate of this disease will decrease in the whole world. Many researchers investigate the diagnosis of heart disease by creating various intelligent medical decision support systems. Artificial neural network concepts represent the highest predictive accuracy over medical data compared to other decision support systems. In this paper, we propose a better prediction method for the existence of heart disease through the scaled conjugate gradient backpropagation of artificial neural networks using K-fold cross-validation. For cardiac datasets, the University of California Irvine (UCI) Machine Learning Repository and IEEE data port have been used. For Cleveland processed heart dataset, the proposed system uses 13 input attributes and provides minimum 63.3803% and maximum 100% accurate results; similarly, for Cleveland Hungarian Statlog heart dataset, the proposed system uses 11 input attributes and provides minimum 88.4754% and maximum 100% accurate results by estimating the presence and absence of heart disease during testing.