Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer

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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 : 12 | 02 | Page : –
    By

    Sayli Rajendra Dholam,

  1. Research Scholar, Thakur Institute of Management , Mumbai, Maharashtra, India

Abstract

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Breast cancer continues to be the most commonly diagnosed cancer among women, with more than 2.3 million new cases diagnosed yearly worldwide. It is stated as the leading causes of cancer-related deaths. Therefore, this emphasizes the dire necessity for early diagnosis with a view to improving survival. Early diagnosis elevates the effectiveness of prediction and treatment. This research carries out a structured and analytical evaluation of various machine learning algorithms, namely, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, and XGBoost for the purpose of breast cancer detection. This study uses the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, one of the standard criterions sets in this field, to classify tumors into benign or malignant. The performance of these algorithms is evaluated in detail using various metrics, namely, accuracy, precision, recall, F-1 score and confusion matrices, to provide scores useful for proper insight of the said groups in terms of their respective capabilities in carrying out diagnoses.

Keywords: Breast Cancer Detection, Machine Learning Algorithms, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, XGBoost, Confusion Matrix, ROC-AUC Analysis, Accuracy, Precision, Recall, F1 score, Wisconsin Diagnostic Breast Cancer (WDBC).

How to cite this article:
Sayli Rajendra Dholam. Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-.
How to cite this URL:
Sayli Rajendra Dholam. Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joaira/article=2025/view=0


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Ahead of Print Subscription Review Article
Volume 12
02
Received 06/03/2025
Accepted 12/04/2025
Published 25/06/2025
Publication Time 111 Days
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