Vol.66, No.1, 2021, pp.603-619, doi:10.32604/cmc.2020.012515
OPEN ACCESS
ARTICLE
FogMed: A Fog-Based Framework for Disease Prognosis Based Medical Sensor Data Streams
  • Le Sun1,*, Qiandi Yu1, Dandan Peng1, Sudha Subramani2, Xuyang Wang1
1 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China
2 Victorian Institute of Technology, Victorian, Australia
* Corresponding Author: Le Sun. Email: LeSun1@nuist.edu.cn
Received 02 July 2020; Accepted 22 July 2020; Issue published 30 October 2020
Abstract
Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However, there are still numerous unresolved issues worth to be explored. For instance, there is a lack of investigation on the disease prediction in fog environment and only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance (e.g., accuracy and time efficiency). To address these issues, we propose a fog-based framework for disease prediction based on Medical sensor data streams, named FogMed. This framework aims to improve the disease prediction accuracy by achieving two objectives: QoS guarantee of fog services and anomaly prediction of Medical data streams. We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed. The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.
Keywords
Ehealth; disease prognosis; fog computing; QoS guarantee
Cite This Article
L. Sun, Q. Yu, D. Peng, S. Subramani and X. Wang, "Fogmed: a fog-based framework for disease prognosis based medical sensor data streams," Computers, Materials & Continua, vol. 66, no.1, pp. 603–619, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.