Review On Enhancing Healthcare Services for Heart Disease Patients using Machine Learning Approaches in Cloud Environment

Vision-based Alarming rates of heart disease affect both sexes. Early warning of heart disease may be obtained by monitoring a variety of risk factors. This new technology is having a profound effect on healthcare systems. As a result of the Internet of Things, we can now remotely monitor patients, gather data, and analyze it to provide superior treatment. However, there is a critical need to provide unique and state-of-the-art secure algorithms for speedy event processing and effective event identification. In this piece, we present a tetrolet ELGamal algorithm (TEA)-based machine learning based logistic Bayesian decision tree (LBDT) for predicting cardiac issues based on existing data saved in the cloud. In addition to providing a safe place to keep sensitive patient data, the cloud may be used as a reliable data source for educational purposes. Comparisons are made between the suggested (LBDT TEA) and other algorithms in terms of encrypting and decrypting times, as well as accuracy, precision, recall, and F-score.