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Bayesian Kernel Methods: Applications in Medical Diagnosis Decision-Making Processes (A Case Study)
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Author(s): Arti Saxena (Manav Rachna International Institute of Research and Studies, Faridabad, India)and Vijay Kumar (Manav Rachna International Institute of Research and Studies, Faridabad, India)
Copyright: 2021
Volume: 6
Issue: 1
Pages: 14
Source title:
International Journal of Big Data and Analytics in Healthcare (IJBDAH)
Editor(s)-in-Chief: Mu-Yen Chen (National Cheng Kung University, Taiwan)
DOI: 10.4018/IJBDAH.20210101.oa3
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Abstract
In the healthcare industry, sources look after different customers with diverse diseases and complications. Thus, at the source, a great amount of data in all aspects like status of the patients, behaviour of the diseases, etc. are collected, and now it becomes the job of the practitioner at source to use the available data for diagnosing the diseases accurately and then prescribe the relevant treatment. Machine learning techniques are useful to deal with large datasets, with an aim to produce meaningful information from the raw information for the purpose of decision making. The inharmonious behavior of the data is the motivation behind the development of new tools and demonstrates the available information to some meaningful information for decision making. As per the literature, healthcare of patients can be analyzed through machine learning tools, and henceforth, in the article, a Bayesian kernel method for medical decision-making problems has been discussed, which suits the purpose of researchers in the enhancement of their research in the domain of medical decision making.
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