The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2025.01601129
PDF

Exploring the Best Machine Learning Models for Breast Cancer Prediction in Wisconsin

Author 1: Abdullah Al Mamun
Author 2: Touhid Bhuiyan
Author 3: Md Maruf Hassan
Author 4: Shahedul Islam Anik

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 1, 2025.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: This research focuses on predicting Wisconsin Breast Cancer Disease using machine learning algorithm, employs a dataset offered by UCI repository (WBCD) dataset. The under- gone substantial preparation, includes managing missing values, normalization, outlier elimination, increase data quality. The Synthetic Minority Oversampling Technique (SMOTE) is used to alleviate class imbalance and to enable strong model training. Machine learning models, include SVM, kNN, Neural Networks, and Naive Bayes, were built and verified using Key performance metrics and K-Fold cv. included as recall, accuracy, F1-score, precision and AUC- ROC were employed to analyze the models. Among these, the Neural Network model emerged the most effective, obtaining a prediction accuracy 98.13%, precision 98.21%, recall 98.00%, F1Score of 97.96%, AUC-ROC score 0.9992. Study underscores promise of ML boosting the diagnosis and treatment of WBCD illnesses, giving scalable and accurate ways for early detection and prevention.

Keywords: Wisconsin breast cancer disease prediction; ML; SVM; KNN; AUC-ROC; Naive Bayes

Abdullah Al Mamun, Touhid Bhuiyan, Md Maruf Hassan and Shahedul Islam Anik, “Exploring the Best Machine Learning Models for Breast Cancer Prediction in Wisconsin” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01601129

@article{Mamun2025,
title = {Exploring the Best Machine Learning Models for Breast Cancer Prediction in Wisconsin},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01601129},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01601129},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Abdullah Al Mamun and Touhid Bhuiyan and Md Maruf Hassan and Shahedul Islam Anik}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org