ItemBook Chapter

An Application of Improved Support Vector Machine Classifier for the Study of Breast Cancer Detection

Title
An Application of Improved Support Vector Machine Classifier for the Study of Breast Cancer Detection
Creator
Natarajan, Jayapandian; Marry, Ann; Yasmin, Huda; Eldo, Jessica; Eswaran, Sivaraman; Zade, Nilima; Krishna Kumar, P.R.
Description
Breast cancer is known to be a major global health challenge, necessitating effective early detection strategies to improve patient outcomes and reduce mortality rates. This research focuses on the application of machine learning algorithms for the detection of breast cancer. The dataset considered includes a wide array of features extracted from breast tissue samples, enabling the evaluation of five different machine learning algorithms. These algorithms were chosen for their proven efficacy in medical diagnostics and their potential to complement traditional diagnostic methods. Among the algorithms evaluated, the support vector machine (SVM) emerged as particularly noteworthy, achieving an impressive accuracy rate of 98.27%. SVM demonstrated robust capabilities in accurately categorising breast cancer cases, effectively distinguishing between benign and malignant tumours with high precision. This underscores SVMs potential as a valuable tool for enhancing breast cancer detection accuracy, thereby aiding clinicians in making informed decisions. Furthermore, this research highlights the importance of leveraging large-scale datasets like WBCD to train machine learning models effectively. Such datasets provide a comprehensive set of features that enable algorithms to discern complex patterns and correlations, which may not be apparent through conventional methods alone. This data-driven approach not only enhances diagnostic accuracy but also lays the groundwork for personalised medicine approaches tailored to individual patient profiles. To summarise, the following study emphasises the transformative role of machine learning in oncology, specifically in early breast cancer detection. Continued research and validation of these algorithms across diverse datasets will be crucial in further improving their effectiveness and applicability in real-world healthcare settings, ultimately benefiting patients globally. 2026 selection and editorial matter, Ravichander Janapati, Usha Desai, Steven Fernandes, Rakesh Sengupta, Shubham Tayal; individual chapters, the contributors.
Source
Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications;pp.166-179
Date
01-01-2025
Publisher
CRC Press
Coverage
Natarajan J., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Marry A., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Yasmin H., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Eldo J., Department of Computer Science and Engineering, Christ University Kengeri Campus, Bangalore, India; Eswaran S., Department of Electrical and Computer Engineering, Curtin University Malaysia, Sarawak, Miri, Malaysia; Zade N., Computer Science and Engineering, Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune, India; Krishna Kumar P.R., Department of Computer Science and Engineering, S.E.A College of Engineering & Technology, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104035969-3; 978-103275324-9;
Format
online
Language
English
Type
Book chapter

Citation

Natarajan, Jayapandian; Marry, Ann; Yasmin, Huda; Eldo, Jessica; Eswaran, Sivaraman; Zade, Nilima; Krishna Kumar, P.R., “An Application of Improved Support Vector Machine Classifier for the Study of Breast Cancer Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24327.

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