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An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care
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Author(s): Rojalina Priyadarshini (KIIT University, India), Rabindra K. Barik (KIIT University, India), Chhabi Panigrahi (University of Rajastan, India), Harishchandra Dubey (The University of Texas at Dallas, USA)and Brojo Kishore Mishra (C. V. Raman College of Engineering, India)
Copyright: 2020
Pages: 13
Source title:
Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch091
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Abstract
This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.
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