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 Article Faculty Publications -Articles 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 Srinivasaiah, Raghavendra; Jankatti, Santosh Kumar; Jinachandra, Niranjana Shravanabelagola; Lamani, Manjunath Ramanna; Lakshmi, Bellam Vijaya; Bhelwa, Rishita Title A name given to the resource Attribute optimization to improve breast cancer prediction using machine learning techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived IAES International Journal of Artificial Intelligence;Volume;15;Issue;2;pp.1327-1338 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v15.i2.pp1327-1338" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v15.i2.pp1327-1338</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036013166?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036013166?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 Srinivasaiah R., Department of Artificial Intelligence and Data Science Engineering, School of Engineering and Technology, CHRIST University, Bangalore, India; Jankatti S.K., Department of Computer Science and Technology, Dayananda Sagar University, Bangalore, India; Jinachandra N.S., Department of Mechanical Engineering, CHRIST University, Bangalore, India; Lamani M.R., Department of Computer Science and Engineering, Moodlakatte Institute of Technology, Kundapura, India; Lakshmi B.V., Department of Computer Science and Engineering, CHRIST University, Bangalore, India; Bhelwa R., Department of Computer Science and Engineering, CHRIST University, Bangalore, India Description An account of the resource Breast cancer (BC) arises when cells grow out of control. It affects women more than men. Seeking cancer treatment can be both costly and time-consuming, with test results spanning from a few hours to several weeks. The duration of these tests depends on the number of attributes within the dataset. This research paper endeavors to optimize the dataset attributes and find the accuracy of the optimized dataset. The primary goal is to reduce features using recursive feature elimination to minimize the time taken for the test result. This work discusses the machine learning technique and the random forest (RF) algorithm, which helps determine the parameter accuracy on the Wisconsin BC diagnostic dataset. The method achieves an accuracy of 96.49% with only eighteen attributes. It has aided the healthcare industry in finding BC in less time and improving the treatment. Copyright (c) 2026 Peddireddy Venkateswara Reddy, Alaguchamy Parivazhagan. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Subject The topic of the resource Attribute optimization; Breast cancer prediction; Machine learning; Random forest classifier; Wisconsin Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online