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Title: Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization
Authors: Akkur, E.
Türk, F.
Eroğul, O.
Keywords: Bayesian optimization
Breast cancer
feature selection
machine learning
Computer aided diagnosis
Decision trees
Learning algorithms
Learning systems
Nearest neighbor search
Support vector machines
Bayesian optimization
Breast Cancer
Breast cancer diagnosis
Features selection
Least absolute shrinkage and selection operators
Machine learning algorithms
Machine learning models
Optimization approach
Feature Selection
Issue Date: 2023
Publisher: Tech Science Press
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. © 2023 CRL Publishing. All rights reserved.
ISSN: 0267-6192
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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