ABSTRACT

Demographic changes, as well as physical difficulties all around the globe, have contributed to the growth in the popularity of digital care delivery. The growth of the Internet of Things (IoT), data, science, plus deep learning has personally helped those technologies. Furthermore, as a consequence of such advancements, huge volumes of data were generated, rendering information systems processing a big concern. The enormous complexity, roughness, plus sparseness among those information make more efficient analysis challenging to execute. Analysis can help with some of these issues. Researchers present a unique statistical methodology with massive healthcare data gathered from IoT portable tech or stored medical record photos throughout this research. Utilizing intermediary among multiple databases as well as MapReduce Hadoop clusters, the suggested solution could adequately solve the information heterogeneity challenges. Moreover, the suggested architecture allows therefore for the usage of mist computers using internet systems to address issues with physical and digital information processing, storing, including categorization. This also ensures that patient medical data was known safely and reliably. Keywords: Internet of Things; health care data; analytic framework; patient medical data; Big data analytics
Publication date: 01/11/2021

https://ijbpas.com/pdf/2021/November/MS_IJBPAS_2021_NOV_SPCL1103.pdf

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https://doi.org/10.31032/IJBPAS/2021/10.11.1103