Title: Leveraging the fog-based machine learning model for ECG-based coronary disease prediction

Authors: R. Hanumantharaju; K.N. Shreenath; B.J. Sowmya; K.G. Srinivasa

Addresses: Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India; Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, India; Affiliated to: Visvesvaraya Technological University, Belagavi, India ' Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, India ' Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India; Affiliated to: Visvesvaraya Technological University, Belagavi, India ' Information Management and Emerging Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India

Abstract: Smart healthcare systems need a remote monitoring system based on the internet of things. Smart healthcare services are an innovative way of synergising the benefits of sensors for large-scale analytics to communicate better patient care. Work provides the sick with healthcare administrations as a sound population through remote observation using detailed calculations, tools and methods for better care. The proposed system integrates architecture based on IoT, fog computing and machine learning (ML) algorithms. The dimensionality of the data collected about heart diseases is loaded, filtered and extracted attributes at the fog layer; the classification model is built at the fog nodes. The resultant of the model is sent to the cloud layer to train classifiers. Cloud layer estimates the level of ML algorithms to predict disease. Result shows that random forest has better feature extraction than naive Bayes with flawlessness of 3% in precision, 3% in recall, and 13% in f-measure.

Keywords: internet of things; IoT; machine learning; random forest; naive Bayes; fog layer; remote monitoring; feature extraction.

DOI: 10.1504/IJBIDM.2022.125214

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.3, pp.328 - 353

Received: 07 Jan 2021
Accepted: 28 May 2021
Published online: 02 Sep 2022 *

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