IoT and Cloud Based health monitoring system Using Machine learning

Abstract

The health care sector is focusing on in-home health care services, where the patients can receive medical care in the privacy of their own home. A patient in a rural region can use a remote health monitoring system to communicate with a doctor in a city who is in a larger city. Machine learning has been used for smart health monitoring systems. They used a wearable sensor to identify a set of five parameters, including Electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection. The technology uses machine learning algorithms to identify doctors for consultation and to identify and predict ailments. In the study, IoT technology and health monitoring have been coupled to give more personalized and responsive health care. The primary purpose of the system is to monitor patients' vital signs in real-time monitoring. The authorized individual can access the patient' s vital signs from their smartphone or PC using a cloud server. The Decision Tree (DT) attained the best accuracy of 99.1 percent after testing the suggested model, which is promising for their purposes. It is observed that the DT achieves best accuracy, while Random Forest is the second-best classifier for this problem.