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2022 | OriginalPaper | Chapter

28. Machine Learning for Classification of Cancer Dataset for Gene Mutation Based Treatment

Authors : Jai Santosh Mandava, Abhishek Verma, Fulya Kocaman, Marian Sorin Nistor, Doina Bein, Stefan Pickl

Published in: ITNG 2022 19th International Conference on Information Technology-New Generations

Publisher: Springer International Publishing

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Abstract

The chapter discusses the significant impact of gene mutations on cancer treatment and the potential of machine learning to automate and enhance the classification of cancer datasets. It provides a comprehensive overview of the historical context and current practices in cancer treatment, focusing on the use of gene mutation-based treatments. The authors present a proposed system architecture that leverages machine learning algorithms to classify genetic variations, significantly reducing the time and effort required for manual analysis. The chapter also includes a detailed comparison of various machine learning classification algorithms and their performance metrics. The experimental results show promising accuracy levels, highlighting the potential of machine learning to revolutionize cancer diagnosis and treatment. The conclusion emphasizes the need for further research to improve model accuracy and expand the dataset to achieve real-world applicability.
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Metadata
Title
Machine Learning for Classification of Cancer Dataset for Gene Mutation Based Treatment
Authors
Jai Santosh Mandava
Abhishek Verma
Fulya Kocaman
Marian Sorin Nistor
Doina Bein
Stefan Pickl
Copyright Year
2022
Book
ITNG 2022 19th International Conference on Information Technology-New Generations

Print ISBN: 978-3-030-97651-4

Electronic ISBN: 978-3-030-97652-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-97652-1_28

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