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    Area of Science:

    • Healthcare Informatics
    • Data Science
    • Internet of Things (IoT)

    Background:

    • Internet of Things (IoT) assisted healthcare systems offer ubiquitous access to electronic health services.
    • Reliable data management is crucial for these systems, facing challenges with heterogeneous data streams containing variations and errors.

    Purpose of the Study:

    • To introduce a novel Proportionate Data Analytics (PDA) method for processing heterogeneous healthcare data streams.
    • To enhance the reliability and response ratio of healthcare services by efficiently handling data variations and errors.

    Main Methods:

    • Developed a Proportionate Data Analytics (PDA) approach for heterogeneous healthcare data stream processing.
    • Utilized linear regression for classifying and segregating data errors from variations within different time intervals.
    • Implemented recurrent differentiation of time intervals post-error detection in data streams.

    Main Results:

    • The proposed PDA method effectively differentiates data streams based on variations and errors.
    • Spontaneous regressions through differentiation and classification maintain a high response ratio for healthcare services.
    • Performance analysis metrics include accuracy, identification ratio, delivery, variation factor, and processing time.

    Conclusions:

    • The Proportionate Data Analytics (PDA) method significantly improves the processing of heterogeneous data streams in IoT-assisted healthcare.
    • Accurate data stream differentiation and classification enhance the overall reliability and efficiency of healthcare services.
    • The approach ensures a high response ratio, crucial for time-sensitive healthcare applications.