Akademik Veri Yönetim Sistemi
International Journal of Intelligent Systems and Applications in Engineering, cilt.6, sa.4, ss.289-293, 2018 (TRDizin)
Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reducethe rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques areused to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used toearly detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and tounderstand how effective these methods are for detection. Methods used can be listed as Artificial Neural Network (ANN), standardExtreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCIlibrary. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistinand chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. Parameters that have the best accuracy values werefound by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end,the results were compared and discussed.