TY - JOUR
T1 - Clouds Proportionate Medical Data Stream Analytics for Internet of Things-Based Healthcare Systems
AU - Kumar, Priyan Malarvizhi
AU - Hong, Choong Seon
AU - Afghah, Fatemeh
AU - Manogaran, Gunasekaran
AU - Yu, Keping
AU - Hua, Qiaozhi
AU - Gao, Jiechao
N1 - Funding Information:
Manuscript received March 10, 2021; revised May 21, 2021 and June 29, 2021; accepted July 31, 2021. Date of publication August 20, 2021; date of current version March 7, 2022. This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grant Information Technology Research Center support program under Grant IITP-2021-0-00742 supervised by the IITP (Institute for Information and communications Technology Planning and Evaluation). (Corresponding author: Choong Seon Hong.) Priyan Malarvizhi Kumar and Choong Seon Hong are with the Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, South Korea (e-mail: mkpriyan@khu.ac.kr; cshong@khu.ac.kr).
Publisher Copyright:
© 2013 IEEE.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.
AB - Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.
KW - Data analytics
KW - IoT
KW - differential computing
KW - healthcare systems
KW - regression learning
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U2 - 10.1109/JBHI.2021.3106387
DO - 10.1109/JBHI.2021.3106387
M3 - Article
C2 - 34415841
AN - SCOPUS:85125965757
SN - 2168-2194
VL - 26
SP - 973
EP - 982
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 3
ER -