A Systematic Literature Review On Heart Disease Prediction Using Blockchain And Machine Learning Techniques

Abstract

One of several major causes of death around the globe is heart disease. Blood pressure, cholesterol level, blood sugar level, heartbeat and body weight are several characteristics that may be monitored in a way to predict heart disease in the earlier stage by using emerging technologies in a better and secure manner that assists while saving lives. Emerging technologies like Machine Learning (ML) and blockchain are revolutionizing the existing healthcare infrastructure, which is a difficult task to securely and accurately forecast heart disease. Blockchain and ML are providing the best solutions to gather information while predicting heart disease. This study provides comprehensive reviews on different ML techniques (Support Vector Machine (SVM), Probabilistic neural network (PNN), Bayesian networks, Convolutional Neural Network (CNN), J48 and Artificial Neural Network (ANN)) in order to predict heart disease. This study proves that SVM works well compared to all other processes and achieves a maximum accuracy of 98.2%.