Smart home health monitoring system for predicting type 2 diabetes and hypertension

Home health monitoring can facilitate patient monitoring remotely for diabetes and blood pressure patients. Early detection of hypertension and diabetes is extremely important, as these chronic diseases often result in life-threatening complications when found at a later stage. This work proposes a smart home health monitoring system that helps to analyze the patient’s blood pressure and glucose readings at home and notifies the healthcare provider in case of any abnormality detected. A combination of conditional decision-making and machine-learning approaches is used to predict hypertension and diabetes status, respectively. The goal is to predict the hypertension and diabetes status using the patient’s glucose and blood pressure readings. Using supervised machine learning classification algorithms, herein a system is trained to predict the patient’s diabetes and hypertension status. After analyzing all the classification algorithms, support vector machine classification algorithm was found to be most accurate and thus chosen to train the model. This proposed work develops an application for a home health monitoring system with a user-friendly easy-to-use graphical user interface to diagnose blood pressure and diabetes status of patients along with sending categorized alerts and real-time notifications to their registered physician or clinic all from home.