Forecasting the Risk of Heart Disease Using Recurrent Neural Network

Cardiovascular disease is a prevalent worldwide occurrence. The accurate identification of this matter holds significant importance for individuals. Cardiac disease advances gradually as a result of the buildup of plaque in the heart, leading to a partial obstruction of blood flow. Cholesterol, calcium, and fibrin are the principal constituents of this plaque. Cardiovascular illness is essential for the flow of oxygenated blood in the human body, facilitating its supply to the heart for pumping. However, the probability of its occurrence can be reduced through meticulous research and timely prediction. The present work investigates the potential application of deep learning methodologies in the early detection and prediction of heart illness. The principal objective of this educational initiative is to accurately ascertain the presence or absence of cardiovascular ailments in an individual. Early-stage forecasting can make use of deep learning approaches, including convolutional neural networks (CNN), multilayer perceptron's, artificial neural networks, and recurrent neural networks. RNN s provide greater classification performance in comparison to other approaches among these strategies.