Hardware and Software Infrastructure for Analysis, Processing and Decision Making in Medical Entities Through the Use of Big Data
Authors: Miguel Angel Quiroz Martinez, Christopher Gustavo Roby Cevallos, Daniel Humberto Plua Moran, Maikel Yelandi Leyva Vazquez
Abstract: Information was analyzed from architectures and models generated by research in big data and IoT for the medical area. The problem is the lack of proposals to have hardware and software infrastructures based on scientific research that assist in the analysis and processing phases to make decisions in medical organizations based on large volumes of data. The main objective of this research was to propose the hardware and software infrastructure for analysis, processing, and decision making in medical entities using large volumes of data. The development proposal of the following research work uses the analytical, inductive, deductive, observation, and quasi-experimental method that allows us to propose a general IoT and Big Data model and architecture for medical entities. This proposal resulted in a General IoT model in the health sector, a General IoT architecture for the health sector, and a Big Data General Architecture for Health Sector. It was concluded that big data and IoT are complemented by data lifecycle management in the capture, storage, processing, and analysis; this management is a conceptual proposition so that other researchers can deepen the design; the physical components of the architecture influence low performance, in part software components assist in high performance; the average performance of the proposed architecture is 93.07%.
Keywords: Decision Making, Medical Entities, Large Volumes Of Data, Internet Of Things, Big Data
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