 An insight into application of big data analytics in healthcareSravani Nalluri and R. Sasikala International Journal of Data Mining, Modelling and Management, 2020, vol. 12, issue 1, 87-117 Abstract: The main aim of this paper is to comprehend, gain insight of the current trends in application of big data in healthcare, and to identify the various potential healthcare horizons. A brief analysis was done on 'big data analytics in healthcare' focusing on collection of data, the tools employed, the aspects of health that were addressed, the type of machine learning algorithms and which statistics commissioned to compare the performance of these algorithms. The focus was mainly on prediction of the diseases, emergency department visits or a disease outbreak, using 'HADOOP' and 'WEKA' tool, by obtaining data from University of California machine learning repository, hospitals and government agencies. Support vector machine, artificial neural networks, naive Bayes and decision tree were commonly used algorithms whose efficacy was compared statistically using 'accuracy'. In my perspective, apart from prediction of disease other domains of health are to be addressed. Keywords: big data; Hadoop; machine learning algorithms; healthcare; map-reduce; chronic diseases; accuracy rate; prevention; analytics. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:12:y:2020:i:1:p:87-117 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
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