An IoT based efficient hybrid recommender system for cardiovascular disease

Abstract

A fog-based IoT model can be helpful for patients from remote areas with cardiovascular disease. An expert cardiologist is usually not available in such remote areas. There are some systems available to classify heart disease and provide recommendations but these existing systems only use classification for recommendations. From this line of research, we propose an IoT based efficient community-based recommender system that diagnoses cardiac disease and its type and provides recommendations related to the physical and dietary plan. The first part intent to collect the data from the patient remotely by using the bio sensors. The IoT based environment is used to transmit the data to the server. Afterward, heart disease prediction model is implemented, that can diagnose the cardiovascular disease and classify into eight available cardiovascular classes i.e. Myocardial Infarction (MI stable), Myocardial Infarction (MI unstable), Acute Coronary Syndrome (ACS), Atrial Fibrillation (AF), Hypertension (HTN), Ischemic Heart Disease (IHD), Left Ventricular Hypertrophy (LVH), Chronic Heart Failure/ Left Ventricle Function (CCF/LVF), Supraventricular Tachycardia (SVT). The second part pursues to provide physical and dietary plan recommendation to the cardiac patient according to gender and age groups. A dataset for diseases and corresponding recommendations is collected from a well-renowned hospital with the help of an expert cardiologist. The performance of the system is evaluated in terms of precision, recall and Mean absolute error and achieves 98% accuracy.