@Article{cmc.2020.012515,
AUTHOR = {Le Sun, Qiandi Yu, Dandan Peng, Sudha Subramani, Xuyang Wang},
TITLE = {FogMed: A Fog-Based Framework for Disease Prognosis Based Medical Sensor Data Streams},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {603--619},
URL = {http://www.techscience.com/cmc/v66n1/40468},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.012515}
}