Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Natarajan, Jayapandian; Marry, Ann; Yasmin, Huda; Eldo, Jessica; Eswaran, Sivaraman; Zade, Nilima; Krishna Kumar, P.R. Title A name given to the resource An Application of Improved Support Vector Machine Classifier for the Study of Breast Cancer Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications;pp.166-179 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003473442-12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003473442-12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022042419?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022042419?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; Technology, Karnataka, Bengaluru, India Description An account of the resource 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. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104035969-3; 978-103275324-9; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online