Heart Disease Detection using Back-propagation Artificial Neural Network

Abstract

Heart disease is a leading health problem in the world, thereby, raising the need for its detection at the earliest stage. The limitations of conventional approaches have motivated the inspire IT professionals and research scholars to execute a promising, simple, inexpensive and easy medical diagnosis system. The present paper proposes a back-propagation artificial neural network (BPANN) based machine learning (ML) technique to enhance the accuracy for the detection of heart disease. The experimental results performed in MATLAB revealed that the proposed system outperforms single-layer and typical ANN. The aim of the study is to design the model with suitable combination of layers and neurons to enhance the success rate. Comparison with other data-analytic parameters and cross-validation method highlights the promising solution for the detection of heart disease.