Breast Cancer Detection Using Machine Learning: A Comparative Analysis of Supervised Learning Algorithms

Year : 2025 | Volume : 12 | Issue : 03 | Page : 46 52
    By

    Niral Mahajan,

  • Ashwini Kukade,

  1. Web Developer, Department of AI, G H Raisoni College of Engineering, Nagpur, India
  2. Professor, Department of AI, G H Raisoni College of Engineering, Nagpur, India

Abstract

Globally, breast cancer remains a predominant cause of mortality among women, highlighting the urgent need for timely and precise diagnostic approaches. This research explores the application of machine learning algorithms—including Logistic Regression, SVM, Naïve Bayes, KNN, and Random Forest—on the Wisconsin Breast Cancer Dataset for effective tumor classification. Key pre-processing steps such as missing value handling, feature scaling, and dimensionality reduction were employed to improve model performance. The study evaluated multiple machine learning models for breast cancer detection using performance metrics such as accuracy, precision, recall, and F1-score. Among the evaluated algorithms, the Naïve Bayes classifier demonstrated the highest accuracy and overall reliability, underscoring its potential for medical diagnostic applications. Feature selection played a critical role in improving model efficiency, highlighting its importance in predictive healthcare analytics. This research emphasizes the value of AI-driven techniques in developing scalable, non-invasive diagnostic systems, which can enhance early detection and clinical decision-making. The findings support integrating machine learning approaches into medical workflows to advance precision medicine and improve patient outcomes.

Keywords: Breast Cancer, Machine Learning (ML), Logistic Regression, Support Vector Machine (SVM), Gaussian Naïve Bayes, K-Nearest Neighbours (KNN), Random Forest

[This article belongs to Research & Reviews: A Journal of Bioinformatics ]

How to cite this article:
Niral Mahajan, Ashwini Kukade. Breast Cancer Detection Using Machine Learning: A Comparative Analysis of Supervised Learning Algorithms. Research & Reviews: A Journal of Bioinformatics. 2025; 12(03):46-52.
How to cite this URL:
Niral Mahajan, Ashwini Kukade. Breast Cancer Detection Using Machine Learning: A Comparative Analysis of Supervised Learning Algorithms. Research & Reviews: A Journal of Bioinformatics. 2025; 12(03):46-52. Available from: https://journals.stmjournals.com/rrjobi/article=2025/view=230979


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 26/03/2025
Accepted 31/08/2025
Published 11/11/2025
Publication Time 230 Days
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