Early Detection of Heart Disease Using Gated Recurrent Neural Network

Cardiovascular disease (CVD) is an important factor in life since it may cause the death of human by effecting the heart and blood vessels of the body. Early detection of this disease is necessary for securing patients life. For this purpose, an automated tool is proposed in this paper for detecting patients with CVD and assisting health care systems also. A stacked-GRU based Recurrent Neural Network model, abbreviated as, stk-G, is proposed in this paper that considers interfering factors from past health records while detecting patients with cardiac problems. This proposed model is compared with two benchmark classifiers known as Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN). The comparative analysis concludes that the proposed model offers enhanced efficiency for heart disease prediction. A promising result is given by the proposed method with an accuracy of 84.37%, F1-Score of 0.84 and MSE of 0.16.