Open Access

ARTICLE

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

by Erkan Akkur¹, Fuat TURK^2,*, 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:

Computer Systems Science and Engineering 2023, 45(2), 1017-1031. https://doi.org/10.32604/csse.2023.033003

Received 06 June 2022; Accepted 13 July 2022; Issue published 03 November 2022

Abstract

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.

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Keywords

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