Predictive Analytics in Cardiovascular Health: Leveraging Deep Learning Algorithms for Early Cardiac Disease Identification

Abstract

Cardiac diseases have remained a major issue in today's era, if diagnosed at some early stages, not only human lives be saved at the initial level of disease, a proactive approach can be employed worldwide accordingly. Nowadays, cardiac diseases are frequent, increasing so rapidly in humans due to improper diet, smoking, lack of exercise, diabetes, people having stress, blood pressure, and more specifically deficient knowledge about the disease occurrence. Most healthcare units lack classification and decision-making techniques to anticipate the disease, consequently unable to perform necessary precautionary measures to decrease the disaster impacts of disease, therefore, it is required to work on such effective approaches having the projection of prior identification of disease and present more reliable decision-making results. The proposed model will provide a reliable Recurrent Neural Network (RNN) approach toward cardiac disease prediction along with present the improvement in the success ratio of the previous research and decrease the possible loss and execution time. This proposed model achieves more than accuracy of 97% in the prediction of cardiac disease at an early stage.