The Adoption of IoMT in Disease Prediction using Machine Learning: A benefit to Healthcare system

Health information needs are also changing the information-fetching behavior, and that is observed around the globe. In this paper author has suggested the possibility of combining edge computing and machine learning algorithms into an Internet of medical things architecture focused on distributed computing. The paper is divided in two parts, First, the author proposed a model for collecting data usually produced by the front-end sensor frameworks on Internet of medical things devices using Edge computing and protocols such as Message Queuing Telemetry Transport (MQTT). Secondly, the author attempted to predict a disease correctly by looking at the patient's symptoms. Four diverse Machine learning algorithms K-Nearest Neighbor (KNN), Naïve Bayes, Decision tree, Random forest tree, were tested. The data set was collected from 2537 patients from the Apollo hospitals of India. To measure the performance, various performances metric is involved, and random forest gives the accuracy of 91%.