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Comparative analysis of machine learning techniques on genetic mutation based cancer diagnosis data

Author(s)
ASHOK REDDY KANDULA Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu-608002, India.
R. SATHYA Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu-608002, India.
S. NARAYANA Department of Computer Science and Engineering, Seshadri Rao Gudlavalleru Engineering College, Seshadri Rao Knowledge Village, Gudlavalleru-521356, India.
Abstract
There are still several research studies about how precision medicine and advanced genetic testing are highly disrupted by how cancer-like diseases are treated. The major disadvantage is that identifying cancer disease by checking gene mutations is a manual process which leads to lot of misclassifications. The paper tries to intend several machine learning techniques to make the manual process into an AI-assisted process that makes the work much easier and efficient. The cancer dataset has a certain kind of complicated format. One such is text category; thus, to address the issue, the paper successfully established a complete data analysis that highly gave a detailed view about the data which is performed in the previous. The article applies several machine learning techniques like Naïve Bayes, K- Nearest Neighbours, and Logistic Regression to classify the data with and without class balancing to analyse the cancer dataset.
Publication Details
Page(s) 1718-1728
DOI DOI not available
Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 6, Year: 2022

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Keywords
Naïve Bayes, Logistic regression, genetic testing, k-Nearest Neighbour, Gene Mutation
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