An Empirical Comparison of Machine Learning Algorithms for Predicting Breast Cancer

Authors : Fatih BASCİFTCİ, Hamit Taner ÜNAL

Pages : 9-20

Doi:10.30516/bilgesci.645067

View : 51 | Download : 14

Publication Date : 2019-12-31

Article Type : Research Paper

Abstract :According to recent statistics, breast cancer is one of the most prevalent cancers among women in the world. It represents the majority of new cancer cases and cancer-related deaths. Early diagnosis is very important, as it becomes fatal unless detected and treated in early stages. With the latest advances in artificial intelligence and machine learning insert ignore into journalissuearticles values(ML);, there is a great potential to diagnose breast cancer by using structured data. In this paper, we conduct an empirical comparison of 10 popular machine learning models for the prediction of breast cancer. We used well known Wisconsin Breast Cancer Dataset insert ignore into journalissuearticles values(WBCD); to train the models and employed advanced accuracy metrics for comparison. Experimental results show that all models demonstrate superior accuracy, while Support Vector Machines insert ignore into journalissuearticles values(SVM); had slightly better performance than other methods. Logistic Regression, K-Nearest Neighbors and Neural Networks also proved to be strong classifiers for predicting breast cancer.
Keywords : Breast cancer, artificial intelligence, machine learning, medical decision support systems

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