HDP-IoT: An IoT Framework for Cardiac Status Prediction System using Machine Learning

The mortality rate is high as a result of the significant increase in the number of various forms of non-communicable heart disorders that occur each year. These illnesses are long-term and gradually progressing, and they might advance to the point where the patient’s health becomes too critical to manage. As a consequence, the vast majority of individuals have a massive heart attack or discover they have a cardiac condition when it is too severe. Therefore, this work presents a cardiovascular status forecasting approach, developed from HDP-IoT and Machine Learning that can gather essential data from the human body using IoT sensors and transport that information to the server, where the information is given with user identification. The data collected from the body must then be normalised before being fed into ml algorithms, which may then be used to quantify and forecast the overall health of a patient’s cardiovascular system. The pulse rate, ECG signal, and cholesterol levels have been gathered from the human body using Internet of Things devices up to this point. The findings of the pulse rate and cholesterol tests received have been pretty excellent.