Open Access

ARTICLE

Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT

K. V. Praveen¹, P. M. Joe Prathap², S. Dhanasekaran³, I. S. Hephzi Punithavathi⁴, P. Duraipandy⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin^7,*

1 Department of Information Technology, St. Peters College of Engineering and Technology, Chennai, 600054, India
2 Department of Information Technology, RMD Engineering College, Chennai, 601206, India
3 Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626128, India
4 Department of Computer Science and Engineering, Sphoorthy Engineering College, Hyderabad, 501510, India
5 Department of Electrical and Electronics Engineering, J B Institute of Engineering and Technology, Hyderabad, 500075, India
6 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, 117997, Russia
7 Department of Logistics, State University of Management, Moscow, 109542, Russia

* Corresponding Author: Denis A. Pustokhin. Email:

(This article belongs to the Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)

Computers, Materials & Continua 2021, 66(2), 1987-2003. https://doi.org/10.32604/cmc.2020.012398

Received 29 June 2020; Accepted 28 July 2020; Issue published 26 November 2020

Abstract

Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security, reliability, and efficiency. The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality. The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things (IoT) in cloud computing environment. The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques. The current research article presents a new Oppositional Glowworm Swarm Optimization (OGSO) algorithm-based clustering with Deep Neural Network (DNN) called OGSO-DNN model for distributed healthcare systems. The OGSO algorithm was applied in this study to select the Cluster Heads (CHs) from the available IoT devices. The selected CHs transmit the data to cloud server, which then executes DNN-based classification process for healthcare diagnosis. An extensive simulation analysis was carried out utilizing a student perspective healthcare data generated from UCI repository and IoT devices to forecast the severity level of the disease among students. The proposed OGSO-DNN model outperformed previous methods by attaining the maximum average sensitivity of 96.956%, specificity of 95.076%, the accuracy of 95.764% and F-score value of 96.888%.

---

Keywords

---

Citations