Iot based patient monitoring and diagnostic prediction tool using ensemble classifier

The ubiquitous growth of Internet of Things (IoT) and its medical applications has improved the effectiveness in remote health monitoring systems of elderly people or patients who need long-term personal care. Nowadays, chronic illnesses, such as, stroke, heart disease, diabetes, cancer, chronic respiratory diseases are major causes of death, in many parts of the world. In this paper, we propose a patient monitoring system for stroke-affected people to minimize future recurrence of the same by alarming the doctor and caretaker on variation in risk factors of stroke disease. Data analytics and decision-making, based on the real-time health parameters of the patient, helps the doctor in systematic diagnosis followed by tailored restorative treatment of the disease. The proposed model uses classification algorithms for the diagnosis and prediction. The ensemble method of tree-based classification-Random Forest give an accuracy of 93%.