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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Vijaya Lakshmi, Bellam
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Computer Science & IT Engineering

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Attribute optimization to improve breast cancer prediction using machine learning techniques Srinivasaiah, Raghavendra; Kumar Jankatti, Santosh; Shravanabelagola Jinachandra, Niranjana; Ramanna Lamani, Manjunath; Vijaya Lakshmi, Bellam; Bhelwa, Rishita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1327-1338

Abstract

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.
Co-Authors Bhelwa, Rishita Kumar Jankatti, Santosh Ramanna Lamani, Manjunath Shravanabelagola Jinachandra, Niranjana Srinivasaiah, Raghavendra