Clouds Proportionate Medical Data Stream Analytics for Internet of Things-Based Healthcare Systems

被引：35
|
作者：
Kumar, Priyan Malarvizhi [1 ]
Hong, Choong Seon [1 ]
Afghah, Fatemeh [2 ]
Manogaran, Gunasekaran [3 ,4 ]
Yu, Keping [5 ]
Hua, Qiaozhi [6 ]
Gao, Jiechao [7 ]
机构：
[1] Kyung Hee Univ, Dept Comp Sci & Engn, Yongin 17104, South Korea
[2] Clemson Univ, Dept Elect & Comp Engn, Clemson, SC 29634 USA
[3] Howard Univ, Dept Elect Engn & Comp Sci, Washington, DC 20059 USA
[4] Asia Univ, Coll Informat & Elect Engn, Taichung 41354, Taiwan
[5] Waseda Univ, Global Informat & Telecommun Inst, Tokyo 1698050, Japan
[6] Hubei Univ Arts & Sci, Comp Sch, Xiangyang 441000, Peoples R China
[7] Univ Virginia, Dept Comp Sci, Charlottesville, VA 22904 USA
来源：
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | 2022年 / 26卷 / 03期
关键词：
Medical services; Diseases; Analytical models; Data models; Computational modeling; Data analysis; Predictive models; Data analytics; differential computing; healthcare systems; IoT; regression learning; PREDICTION; MODEL;
D O I：
10.1109/JBHI.2021.3106387
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：973 / 982
页数：10
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