Intelligent Framework for Prediction of Heart Disease using Deep Learning

Abstract

Heart diseases pose a serious threat. When arteries that supply oxygen and blood to the heart are completely blocked or narrowed, the cardiac issue happens. The prominent causes of death have been cardiac disease. In a short period, the mortality rate has spiked. Cardiovascular diseases refer to these heart-associated diseases. These diseases are seen more in developing rather than developed countries. Inaccurate diagnosis of the disease may cause fatalities, and hence, precision and safety in diagnosing heart disease would be the prime factor in healthcare practice. In the proposed study, deep learning-based diagnosis system for heart disease prediction is proposed. The proposed classifier model achieves the accuracy for sensitivity with 98.21% the specificity achieving the value of 97.85%, the precision value of 98.41%, recall 97.43%, and 97.09% of accuracy. The BP-NN with mRmR feature extraction obtained a high accuracy rate when compared with the BP-NN classifier without a feature selection process. From the above-obtained results, mRmR with BP-NN algorithm obtains better result compared to the existing algorithms.