Comparative performance analysis of various classifiers on a breast cancer clinical dataset

Abstract

Breast cancer is a major threat disease among women nowadays. It does not have any specific age to occur, and it is more common disease and causing death to many women. The highly influence of breast cancer occurrence is age, and age determines the treatments to be given and the lifetime of the patients. In this paper, we have considered age at diagnosis as the main feature for our analysis. We have performed a comparative analysis among the three classifiers such as Random Forest, Decision Tree, and Naive Bayes on the Metabric clinical dataset and obtained the rate of accuracy for each classifier.