IoT and Machine Learning Based Health Monitoring and Heart Attack Prediction System

Abstract

The increased use of mobile technologies and smart devices in the area of health has caused great impact on the world. Health experts are increasingly taking advantage of the benefits of these technologies and hence generating a significant improvement in health care in clinical settings. Internet of things (IoT) and machine learning techniques can be used efficiently for this purpose. The objective of this work is to design and develop a real-time IoT based health monitoring and heart attack prediction system that integrates vital signs sensors, location sensors, ad-hoc networking and web portal technology to allow remote monitoring of patient's health status and to predict the heart disease through various machine learning techniques. In this work it has ensured the correct and efficient transmission of the vital signs data to the ThingSpeak server through the internet via a given access point(AP) and notified the user of the same through the GSM module. A heart attack prediction system is also developed to predict the probability of heart attack from the available parameters. The novelty of the proposed work is that it takes the advantage of both the IoT and machine learning technology to monitor and predict the diseases. The proposed system is inexpensive too.