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Big Data Analysis using Machine Learning for Health Systems

by Deepak Gupta, Shikha Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 9
Year of Publication: 2023
Authors: Deepak Gupta, Shikha Gupta
10.5120/ijca2023922745

Deepak Gupta, Shikha Gupta . Big Data Analysis using Machine Learning for Health Systems. International Journal of Computer Applications. 185, 9 ( May 2023), 8-13. DOI=10.5120/ijca2023922745

@article{ 10.5120/ijca2023922745,
author = { Deepak Gupta, Shikha Gupta },
title = { Big Data Analysis using Machine Learning for Health Systems },
journal = { International Journal of Computer Applications },
issue_date = { May 2023 },
volume = { 185 },
number = { 9 },
month = { May },
year = { 2023 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number9/32728-2023922745/ },
doi = { 10.5120/ijca2023922745 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:39.185130+05:30
%A Deepak Gupta
%A Shikha Gupta
%T Big Data Analysis using Machine Learning for Health Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 9
%P 8-13
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The growth of big data in the healthcare industry presents new opportunities for data-driven decision making, but also poses significant challenges due to the complexity and heterogeneity of health data. Big data analysis itself is challenging task for real time applications and involving machine learning make it more rigid towards the solution. But believing that combining both will provide the effective and efficient solution and intelligence decision making. In this study, we review the current state-of-the-art in big data analytics and the learning model for decision support system using machine learning techniques for healthcare, and we discuss their potential impact on healthcare delivery and patient outcomes. Our framework addresses key challenges in healthcare data, such as missing data, high dimensionality, and class imbalance. We also discuss ethical and regulatory considerations for big data in healthcare, and we propose a set of best practices for ensuring patient privacy and data security. An initial survey has been carried out to define the field for big data. Hadoop tools has been used for data analysis from various hospitals as it provides us an efficient way to store data in a distributed fashion which reduce the high storage personal units like drives and others. Supervised learning with hidden markov model is suggested in this approach for intelligent behavior of proposed model.

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Index Terms

Computer Science
Information Sciences

Keywords

MapReduce Machine learning Q-learning Hadoop Parallel processing.

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