Computer Aided Diagnosis of Breast Cancer using Machine Learning Techniques

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 15 | Issue : 02 | Page : –
    By

    S. Kavitha,

  • N.Vaijayanthi,

  1. Assistant Professor, Department of Computer Science and Engineering, J.J College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
  2. Dean, Department of Electronics and Communication Engineering, Indra Ganesan College of Engineering, Tiruchirappalli, Tamil Nadu, India

Abstract

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Breast cancer is one of the significant health problems that lead to early mortality in women, especially those between 40 and 55 years of age all over the world. In recent years, the number of breast cancer cases among women has risen significantly, making early and accurate diagnosis more important than ever. Computer-aided diagnostic (CAD) tools have become valuable in supporting radiologists by enhancing the precision of breast cancer detection. This study introduces a CAD algorithm that uses machine learning techniques to classify breast tumors as either benign or malignant. It also explores how feature selection affects classification accuracy, using the Minimum Redundancy Maximum Relevance (mRMR) method to rank and select the most important features. The algorithm is evaluated using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset.. The performance of machine learning models such as Linear Discriminant Analysis (LDA), K Nearest Neighbor (KNN), Naive Bayes Classifier (NB),  Support Vector Machine (SVM), and Decision Tree (DT) classifier are compared. The experimental results show that when implemented with an LDA classifier with feature selection, the proposed algorithm achieves the maximum accuracy of 97.6% compared to other machine learning (ML) models.

Keywords: Breast cancer, diagnosis, machine learning, feature selection, classification, accuracy

[This article belongs to Nano Trends – A Journal of Nano Technology & Its Applications ]

How to cite this article:
S. Kavitha, N.Vaijayanthi. Computer Aided Diagnosis of Breast Cancer using Machine Learning Techniques. Nano Trends – A Journal of Nano Technology & Its Applications. 2025; 15(02):-.
How to cite this URL:
S. Kavitha, N.Vaijayanthi. Computer Aided Diagnosis of Breast Cancer using Machine Learning Techniques. Nano Trends – A Journal of Nano Technology & Its Applications. 2025; 15(02):-. Available from: https://journals.stmjournals.com/nts/article=2025/view=0


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Regular Issue Subscription Original Research
Volume 15
Issue 02
Received 25/03/2025
Accepted 03/04/2025
Published 26/05/2025
Publication Time 62 Days
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