A Comparison of the Diagnosis Performance of Machine Learning Algorithms on the Breast Cancer Wisconsin Dataset
Received: 29 July 2025 | Revised: 18 August 2025 and 8 September 2025 | Accepted: 9 September 2025 | Online: 20 November 2025
Corresponding author: Ponnuru Sowjanya
Abstract
Breast cancer survival rate can be dramatically affected if the disease is diagnosed early and the stage is classified accurately. Model-driven or statistical-driven Machine Learning (ML) techniques are powerful and reliable methods of diagnosing breast cancer, with the ability to utilize, sort, and detect patterns in data, and to handle immense databases. This research work considers the Breast Cancer Wisconsin (BCW) dataset to train several ML models (Random Forest, Support Vector Machines, Gradient Boosting, Logistic Regression, k-Nearest Neighbors) to classify images as either benign or malignant and compares the results. The followed methodology involves data preprocessing and feature extraction. All tested algorithms performed satisfactorily with Random Forest surpassing the others.
Keywords:
breast cancer, machine learning, diagnosis, classification, Breast Cancer Wisconsin DatasetDownloads
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Copyright (c) 2025 Sreerama Murty Maturi, C. Dastagiraiah, Ponnuru Sowjanya, Chanumolu Kiran Kumar, J. S. V. R. S. Sastry
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