Turgut, S.Dagtekin, M.Ensari, T.2025-05-102025-05-102018978153865135310.1109/EBBT.2018.83914682-s2.0-85050231482https://doi.org/10.1109/EBBT.2018.8391468https://hdl.handle.net/20.500.14720/5126In this study We used microarray breast cancer data for classification of the patients using machine learning methods. First, 8 different machine learning algorithms are applied to the data, without applying any feature selection methods. Then two different feature selection methods are applied. The results of the classifications are compared with each other and with the results of the first case. The methods applied are SVM, KNN, MLP, Decision Trees, Random Forest, Logistic Regression, Adaboost and Gradient Boosting Machines. After applying the two different feature selection methods with the best 50 features are applied, SVM gave the best results. MLP is applied using different number of layers and neurons to examine the effect of the number of layers and neurons on the classification accuracy. It is determined that the increase in the number of layers sometimes decreased, sometimes didn't change the accuracy. © 2018 IEEE.eninfo:eu-repo/semantics/closedAccessFeature Selection MethodsMachine Learning AlgorithmsMicroarray TechnologyConference ObjectN/AN/A13