Integrating feature selection and mislaid data in thyroid classification using data mining algorithms

Automated diagnostic systems have become increasingly popular in the healthcare industry in recent years. In recent years, these systems have gained popularity for their diagnostic benefits. The operator-dependent existence of medical imaging systems can be minimized with this method, and the diagno...

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Bibliographic Details
Published in:AIP Conference Proceedings 2022-11, Vol.2516 (1)
Main Authors: Parimala S., Vadivu, P. Senthil
Format: Article
Language:English
Subjects:
Data mining
Diagnostic systems
Medical imaging
Scientific papers
Sorting algorithms
Online Access:Get full text
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Summary:Automated diagnostic systems have become increasingly popular in the healthcare industry in recent years. In recent years, these systems have gained popularity for their diagnostic benefits. The operator-dependent existence of medical imaging systems can be minimized with this method, and the diagnostic process can be made more repeatable. In addition, it aids in the improvement of diagnostic accuracy. This effectively works with features that cannot be obtained by visual analysis or intuitive examinations (such as computational and statistical features). In this paper, we propose a new algorithm called "Optimized Weighted KNNI algorithm" (OWKNNI). In this research paper, we have addressed the topic of migration. This paper discusses the problem of dimensionality reduction and the use of feature selection algorithms, which is one of the most important tasks in feature engineering. The solution is given sorting out the problem is proposed as an algorithm.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0109424