DEEP-CARDIO: Recommendation System for Cardiovascular Disease Prediction Using IoT Network

Abstract

The Internet of Things (IoTs)-based remote healthcare applications provide fast and preventative medical services to the patients at risk. However, predicting heart disease is a complex task, and diagnosis results are rarely accurate. To address this issue, a novel Recommendation System for Cardiovascular Disease (CVD) Prediction Using IoT Network (DEEP-CARDIO) has been proposed for providing prior diagnosis, treatment, and dietary recommendations for cardiac diseases. Initially, the physiological data are collected from the patients remotely by using the four biosensors, such as ECG sensor, pressure sensor, pulse sensor, and glucose sensor. An Arduino controller receives the collected data from the IoT sensors to predict and diagnose the disease. A CVD prediction model is implemented by using bidirectional-gated recurrent unit (BiGRU) attention model, which diagnoses the CVD and classifies into five available cardiovascular classes. The recommendation system provides physical and dietary recommendations to cardiac patients based on the classified data, via user mobile application. The performance of the DEEP-CARDIO is validated by Cloud Simulator (CloudSim) using the real-time Framingham’s and Statlog heart disease dataset. The proposed DEEP CARDIO method achieves an overall accuracy of 99.90%, whereas the MABC-SVM, HCBDA, and MLbPM methods achieve 86.91%, 88.65%, and 93.63%, respectively.