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An Exhaustive Inference of Machine Learning Applications in Healthcare: Analyzing Research Studies on Diagnosis and Prevention
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Author(s): Manas Kumar Swain (Siksha O Anusandhan University, India), Narendra Kumar Kamila (GITA College, India), Lambodar Jena (Siksha O Anusandhan University, India)and Nilamadhab Mishra (VIT Bhopal University, India)
Copyright: 2025
Pages: 22
Source title:
Driving Global Health and Sustainable Development Goals With Smart Technology
Source Author(s)/Editor(s): Mohit Kukreti (University of Technology and Applied Sceinces, Oman), Sabina Sehajpal (Chandigarh University, India), Rajesh Tiwari (Graphic Era University, India)and Kiran Sood (Chitkara University, India)
DOI: 10.4018/979-8-3373-0240-9.ch007
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Abstract
Machine learning has become an important tool in healthcare research to solve complex classification problems effectively, efficiently, and quickly. Generally, doctors treat patients according to their medical knowledge and personal experience. Since different professionals have different experiences, they may sometimes make a wrong diagnosis and need more time for treatment. Current research mainly focuses on the problem of classifying/predicting medical data based on machine learning. There is a need to create an intelligent structure that can distribute the information stored in the database. Human data analysis capabilities are less compared to data storage. This is more important in the case of medical records because it helps search, diagnose, and treat patients based on individual records. This paper's main goal is to review the pre-researched methodologies of machine learning techniques to analyze healthcare data to diagnose and prevent illnesses. Finally, these research articles are classified based on healthcare data, machine learning techniques, and performance parameters.
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