Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization Erkan Akkur 1 , Fuat TURK 2,* and Osman Erogul 1 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: fuatturk@karatekin.edu.tr Received: 06 June 2022; Accepted: 13 July 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 classifica- tion 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 For- ward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimi- zation approach to obtain optimal hyperparameter values. Experimental results showed the uni fied 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 outper- forming results compared to recent studies. Keywords: Breast cancer; machine learning; Bayesian optimization; feature selection 1 Introduction Breast cancer (BC) have been considered as the most diagnosed malignant disease among females in recent years [1]. Abnormal growths of some cell in the breast tissue can cause breast cancer. Benign and malign tumors are abnormally growing cells in the breast tissue. Benign tumor is noncancerous which can be treated with medicine or surgery. However, malignant tumor shows the characteristics of cancer. When malignant tumor is not treated, it can rapidly spread to organs and increase the mortality rates. Therefore, early diagnosis of BC is a necessary step to reduce the mortality rates [2]. Methods such as 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. Computer Systems Science & Engineering DOI: 10.32604/csse.2023.033003 Article ech T Press Science