A hybrid edge-assisted machine learning approach for detecting heart disease

Abstract

Various resources are provided by cloud computing over the Internet, which enable plenty of applications to be employed to offer different services for industries. However, cloud computing due to the relying on a central server/datacenter has limitations such as high latency and response time, which are so crucial in real time applications like healthcare systems. To solve this, edge computing paradigm paves the way and provides pioneering solutions by moving the computational and storage resources closer to the end users. Edge computing by facilitating the realtime applications becomes more suitable for healthcare systems. This paper uses edge technology for detecting heart disease in patients utilizing a hybrid machine learning method. Although there exist some works in this area, there is still a need for improving the prediction accuracy. To this end, this paper proposes a metaheuristic-based feature selection method using Black Widow Optimization (BWO) algorithm, and then, applies different classifiers on the selected features. The experimental results show that AdaBoost classifier along with BWO-based feature selection by 90.11 % accuracy outperforms other experimental methods, such as KNN, SVM, DT, and RF.