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Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques

Author

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  • Meshwa Rameshbhai Savalia

    (Institute of Technology, Nirma University, India)

  • Jaiprakash Vinodkumar Verma

    (Institute of Technology, Nirma University, India)

Abstract

Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus saving the lives of many people. This paper proposed four different models, built using different combinations of selected features and applying five ML classification techniques to all the models to identify the best model with the highest accuracy. It analyzes five machine learning techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), and k-nearest neighbor (KNN), for prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer Dataset on these four models. The objective of the paper is to find the best ML algorithm that can most accurately predict breast cancer for a particular model. The outcome of this paper helps the doctors to improvise the diagnosis by knowing the effect of combinations of symptoms with the growth of breast cancer.

Suggested Citation

  • Meshwa Rameshbhai Savalia & Jaiprakash Vinodkumar Verma, 2023. "Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 12(1), pages 1-19, January.
    • Handle: RePEc:igg:jrqeh0:v:12:y:2023:i:1:p:1-19
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    References listed on IDEAS

    as
    1. Saba Bashir & Usman Qamar & Farhan Khan, 2015. "Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2061-2076, September.
    2. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.
    3. Min-Wei Huang & Chih-Wen Chen & Wei-Chao Lin & Shih-Wen Ke & Chih-Fong Tsai, 2017. "SVM and SVM Ensembles in Breast Cancer Prediction," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
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