IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v11y2024i15p124-138.html
   My bibliography  Save this article

Comparison on Three Supervised Learning Algorithms for Breast Cancer Classification

Author

Listed:
  • Noramira Athirah Nor Azman

    (College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA Perak Branch, Tapah Campus, Malaysia)

  • Mohd. Faaizie Darmawan

    (College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA Perak Branch, Tapah Campus, Malaysia)

  • Mohd Zamri Osman

    (Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia)

  • Ahmad Firdaus Zainal Abidin

    (Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia)

Abstract

Breast cancer is a widespread and potentially deadly illness that affects many women worldwide. The uncontrolled growth of cells in breast tissue results in the formation of tumors, categorized as malignant or benign. Malignant tumors pose the greatest threat due to their potential to result in fatality. Early detection becomes crucial in preventing adverse outcomes, ensuring swift access to suitable treatment, and mitigating the progression of tumors. Traditionally, breast cancer diagnoses relied on medical expertise, introducing the risk of human error. Hence, this study addresses this challenge by employing supervised machine-learning models using a Support Vector Machine (SVM), K-nearest neighbors (KNN), and Random Forest (RF), to classify breast cancer as benign or malignant. These algorithms are chosen due to their proficiency in handling classification tasks. This study aims to implement 10-fold Cross-validation to the three models chosen to ensure model robustness applied to Breast Cancer Wisconsin (Diagnostic) dataset. Accuracy score is chosen as the performance measures for the models. Based on the results, RF with 500 and 1000 estimators outperformed SVM and KNN with an accuracy of 96.31%, compared to SVM (95.61%) and KNN (93.15%). To conclude, this study’s outcomes have the potential to significantly contribute to the development of an automated diagnostic system for early breast cancer detection.

Suggested Citation

  • Noramira Athirah Nor Azman & Mohd. Faaizie Darmawan & Mohd Zamri Osman & Ahmad Firdaus Zainal Abidin, 2024. "Comparison on Three Supervised Learning Algorithms for Breast Cancer Classification," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(15), pages 124-138, August.
  • Handle: RePEc:bjc:journl:v:11:y:2024:i:15:p:124-138
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-11-issue-15/124-138.pdf
    Download Restriction: no

    File URL: https://rsisinternational.org/journals/ijrsi/articles/comparison-on-three-supervised-learning-algorithms-for-breast-cancer-classification/
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:11:y:2024:i:15:p:124-138. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.