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Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Erkan Akkur1, Fuat TURK2,*, Osman Erogul1

1 Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara, 06560, Turkey
2 Deparment of Computer Engineering, Karatekin University, Çankırı, 18100, Turkey

* Corresponding Author: Fuat TURK. Email: email

Computer Systems Science and Engineering 2023, 45(2), 1017-1031.


Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.


Cite This Article

E. Akkur, F. TURK and O. Erogul, "Breast cancer diagnosis using feature selection approaches and bayesian optimization," Computer Systems Science and Engineering, vol. 45, no.2, pp. 1017–1031, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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