Towards Improving Patient Health Monitoring System using Machine Learning and Internet of Things

In today’s world, everyone’s health is a major concern and a top priority. Humans are afflicted with a plethora of diseases because of their unhealthy habits. People are primarily affected by heart attacks and low oxygen levels because of poor medical care and late diagnosis. As a result, this work aims to combat such untimely deaths using smart health monitoring, which employs machine learning and IoT. The proposed system includes ThingSpeak cloud to communicate with the doctor in case of any emergency. This system consists of body temperature sensor, pulse oximeter sensor (for collecting heartbeat rate and oxygen level) and blood pressure sensing module for tracking patient’s health. These sensors are interfaced with the Raspberry pi and Arduino Uno microcontroller. The obtained result from patients is continuously monitored and it is updated in LCD and doctor’s webpage using Internet of Things. Following these steps, a trained Machine Learning model is used to determine the type of disease being experienced by the patient. This system predicts Normal and two major disease namely Hypertension and Lung disease. By incorporating all these features, we can ensure that people who suffer from heart attacks and lung disease will not die suddenly. The accuracy of this proposed method is 86% approximately in a real time scenario. Furthermore, because raw medical data can be analyzed in a short period of time, the work will aid clinicians in remote monitoring during epidemic situations such as covid.