Open Access

ARTICLE

Comparative Evaluation of Data Mining Algorithms in Breast Cancer

Fuad A. M. Al-Yarimi^*

Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia

* Corresponding Author: Fuad A. M. Al-Yarimi. Email:

Computers, Materials & Continua 2023, 77(1), 633-645. https://doi.org/10.32604/cmc.2023.038858

Received 31 December 2022; Accepted 17 April 2023; Issue published 31 October 2023

Abstract

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and accuracy of various algorithms for breast cancer diagnosis such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, Additive Regression (AR), Back Propagation Neural Network (BP), and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the author found that Support Vector Machine and Bagging had the highest precision and accuracy, respectively. Also, assess the number of studies that provide machine learning techniques for breast cancer detection.

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Keywords

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