Breast Cancer Detection Using Decision Tree, Naïve Bayes, KNN and SVM Classifiers: A Comparative Study

The breast cancer remains as the major cause for the fatality in the women. To predict and classify breast cancer at early stage, researchers have used various machine learning procedures. Various classification algorithms like decision tree, SVM, KNN have been used on breast cancer dataset to categorize a cancer stage as either nonthreatening or threatening. In this paper, the four basic models of classification i.e. decision tree, Naïve Bayes, KNN, SVM have been used to classify the cancer stage. Then, a comparative study is done on these classifier using different accuracy measures like precision, recall and f1-score. This study shows that decision tree classifiers are best among all classifiers in terms of accuracy.