Automatic autonomous heart monitoring device using machine learning for COVID patients.

In today's world of COVID infection, heart monitoring should be done to Quarantine patients at home from time to time on daily basis either by an attendee or a nurse. It is becoming increasingly tedious for people to be at home to take care of their loved ones, as their jobs don't permit them to do so and also due to the infection spread. To tackle this issue, we present the concept of a heart monitoring device with line follower robot navigation. This setup is used to check elderly and bed-ridden patients automatically at a pre-defined time with the help of an Arduino controller. Also in this work, we bring forth a novel method to improve the accurateness in predicting the heart condition using machine learning procedures. The prediction model is built using a variety of feature combinations and classification approaches. The datasets are processed using Python programming and two core Machine Learning Algorithms, namely the Random Forest Algorithm and the Gradient Boosting Tree Algorithm, which reveals that the Random Forest Algorithm is the best algorithm among these two in terms of heart disease exactness. [ABSTRACT FROM AUTHOR]