Title: Intelligent healthcare data segregation using fog computing with internet of things and machine learning

Authors: Amit Kishor; Chinmay Chakraborty; Wilson Jeberson

Addresses: Department of Computer Science and Information Technology, Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), Allahabad, U.P., India ' Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Jharkhand, India ' Department of Computer Science and Information Technology, Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), Allahabad, U.P., India

Abstract: As the population worldwide is increasing exponentially, chronic disease is also growing very rapidly in the world. Thus, the growth of technological development is providing a better and effective solution for chronic diseases. Day by day, the development of technology enhancement is impacting the use of technology in healthcare. However, in today's scenarios, the use of cloud computing and the internet of things (IoT) sensor is very useful in patient monitoring application of healthcare. In this work, we propose a fog computing model in addition to cloud computing and IoT sensors to improve the latency in healthcare. Here, we use a random forest (RF) machine learning approach for intelligently segregation of patient data and improving the latency using fog computing. The main purpose of the proposed work is to take personal care of the patient with minimum time in the real-time domain. To get this, RF classifiers are trained and mapped with data collected through IoT sensors. This model reduces the unnecessary visits of patients to different doctors' clinics and it saves time and money. The model achieves a 92%-95% overall reduction in latency in comparison to the prior art of work.

Keywords: machine learning; internet of things; IoT; fog computing; cloud computing; healthcare.

DOI: 10.1504/IJESMS.2021.115533

International Journal of Engineering Systems Modelling and Simulation, 2021 Vol.12 No.2/3, pp.188 - 194

Received: 13 Oct 2020
Accepted: 13 Nov 2020

Published online: 28 May 2021 *

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