Health Monitoring System Using IOT and Machine Learning

Abstract - Health is one of the very basic needs to any individual. As per WHO statistics, 24% of deaths in India and one- third of all global deaths due to heart diseases. Around 17 million people die due to cardiovascular disease every year. Heart Disease is one of the most effective diseases in the world today. For physical consultant, it is complicated to predict chronic disease on right time. The diagnosis of disease through traditional medical history has been considered as been as not reliable in many aspects. To predict the disease, non-invasive based methods based on machine learning techniques are reliable and efficient. A secure IoT based healthcare system using BSN (Body Sensor Network). The components of IoT and BSN have the ability to collect and transfer data over network with requiring any assistance. BSN-Care addresses the security concerns associated with transmission of sensitive (life-critical) data over the network. This work also provide analysis as well as disease recommendation of patient data. In our system we are measuring patient’s parameters (ECG, temperature and heart rate etc) different available sensors data. The current survey shows that mortality rate increasing in vast amounts because of heart disease. So, to minimize the mortality rate intelligent heart disease prediction system is required. There are various reasons for heart disease like changing lifestyle, more stress and so on. So, heart disease prediction very important needs of life. As we have studied in literature various data mining techniques have been used for the prediction of heart disease. Parameters considered for experiment are heart rate, ECG and temperature so on. Each day in large quantity medical data is generated so important knowledge extraction from this big data is challenging task. Heart is the main part of human life, if heart is working properly then human health is good. Keywords- - Real Time Data, IoT, Machine Learning, Wearable Sensors, Remote Patient Monitoring, Anomaly Detection, Predictive Modeling, Data Analysis, Health Monitoring