Intelligent applications of cloud computing in enhancing health care services

Abstract

In this work, a thorough evaluation of an approach to extract protocols in a series of time data has been presented based on Biological parameters in medical situations. This methodology is wholly data-enhanced with its prototypical pattern mined during every Biological timeframe dataset. The produced rules, centered on the prototypical design have been illustrated in their connection to the wide range of prototypical data arrangements. With that regard, this paper provides an analysis of an approach to effectively measure the similarity of protocol arrangements which have been introduced to verify the uniqueness of protocol arrangements. This approach is evaluated based on Biological records from the medical category in the Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC) online databases such as respiratory failure, sepsis, or angina. The findings in this analysis indicate that data mining approaches can receive a distinctive framework for every medical situation and represent the produced mining rules in an understandable and contextual format.