Review

Machine learning approach on healthcare big data: a review

  • Received: 30 June 2020 Accepted: 15 October 2020 Published: 29 October 2020
  • In the past few years, big data has flattering more dominant in healthcare, due to three major reasons, such as the huge amount of data available, expanding healthcare costs, and a target on personalized care. Big data processing in healthcare refers to generating, collecting, analyzing, and holding clinical data that is too vast or complex to be inferred by classical means of data processing methods. Big data sources for healthcare include, the Internet of Things (IoT), Electronic Medical Record/Electronic Health Record (EMR/EHR) contains patientos medical history, diagnoses, medications, treatment plans, allergies, laboratory and test results, genomic sequencing, Medical Imaging, Insurance Providers and other clinical data. This paper discusses different machine learning algorithms that were applied to various healthcare data. Also, the challenges of processing, handling big data, and their applications. The scope of the paper is to elaborate on the application of machine learning algorithms and the need for handling and utilizing big data from a different perspective.

    Citation: M Supriya, AJ Deepa. Machine learning approach on healthcare big data: a review[J]. Big Data and Information Analytics, 2020, 5(1): 58-75. doi: 10.3934/bdia.2020005

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  • In the past few years, big data has flattering more dominant in healthcare, due to three major reasons, such as the huge amount of data available, expanding healthcare costs, and a target on personalized care. Big data processing in healthcare refers to generating, collecting, analyzing, and holding clinical data that is too vast or complex to be inferred by classical means of data processing methods. Big data sources for healthcare include, the Internet of Things (IoT), Electronic Medical Record/Electronic Health Record (EMR/EHR) contains patientos medical history, diagnoses, medications, treatment plans, allergies, laboratory and test results, genomic sequencing, Medical Imaging, Insurance Providers and other clinical data. This paper discusses different machine learning algorithms that were applied to various healthcare data. Also, the challenges of processing, handling big data, and their applications. The scope of the paper is to elaborate on the application of machine learning algorithms and the need for handling and utilizing big data from a different perspective.
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    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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