Classification Comparison of Machine Learning Algorithms Using Spectroscopic Breast Tissues Dataset

Breast cancer is a very common disease today. And early diagnosis is of great importance in order to prevent this disease. It is aimed to prevent cancer types by classification with artificial intelligence and machine learning algorithms to help with early diagnosis. The spectroscopy results of 106 individuals in this study were used. In this study, 6 different machine learning algorithms were applied to the data in 2 steps: (1) In the first step, it is aimed to define the data set as test and training data and to find out which algorithm is more successful; (2) In the second step, it is aimed to find the classification algorithm that gives the best result by re-applying the cross-validation machine learning algorithms to the data. According to the AUC results of the SVM algorithm (Step-1: 99.6%, Step-2: 99.8%) in classification, it was seen that it showed high success in both steps.