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

Priyan Malarvizhi Kumar, Choong Seon Hong*, Fatemeh Afghah, Gunasekaran Manogaran, Keping Yu, Qiaozhi Hua, Jiechao Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)973-982
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number3
Publication statusPublished - 2022 Mar 1


  • Data analytics
  • IoT
  • differential computing
  • healthcare systems
  • regression learning

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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