An Analytical Review of Heart Failure Detection based on IoT and Machine Learning

Chronic heart failure (CHF) is a worldwide disease that affects more than 26 million people in the developed world. Tracking complications such as cardiovascular failure patients could be used to act in a preventative manner, to improve early diagnosis, and to avoid the need for hospitalisation or even life threatening circumstances, thereby significantly enhancing the patient’s overall quality of life. The most current improvements in computer-aided, heart sound recognition technologies have been examined in this system, which has been in operation for the past decade. This research investigates approaches for detecting CHF based on the heart sounds produced by the patient. The perception of heart rate, as well as the relationship between heart sounds and cardiovascular disease, are important considerations. The basic techniques used in the processing and interpretation of cardiac signals seem to be de-noising, categorization, extraction, feature extraction, and classification, among others. Because of the emphasis on the usage of Machine Learning (ML) algorithms for analysing heart sounds, classic Machine-Learning (ML) technologies are merged with IoT end-to-end technologies, and both are integrated with a wide range of defined techniques. The primary goal of this study is to examine the many technologies that are comprised of the internet of things that are used to forecast heart attack disease and how they are used. It is the purpose of this study not only to explain the existing heart attack prediction, but also to address the aware and monitoring system for the patient who is likely to be suffering from cardiovascular illness. In this work, certain types of literature are reviewed and a survey on approaches for predicting heart attack occurrences in the future has been conducted.