Evaluating the Performance of Supervised Machine Learning Algorithms in Breast Cancer Datasets

Abstract

Breast cancer is the leading cause of mortality globally. Several attempts have been made to use data mining methodology together with machine learning techniques to develop systems that can detect or prevent breast cancer. In line with the reviewed paper; large datasets for illness analysis have been developed. In this study, the results of selected Machine Learning algorithms are compared: Decision Table, J48, SGD, bagging, and Naïve Bayes Updateable on Wisconsin Breast Cancer Original dataset was conducted using weka tools. Exploratory data analysis, pre-processed with supervised attribute selection and class order, was used to identify potential features to aid the performance of the chosen algorithms in classification. The empirical result showed that Decision Table explores greater likelihood (74% correctly classified instances, True Positive Rate of 0.752, False Positive Rate of 0.478, Precision of 0.77, receiver operating characteristic Area of 0.682) in terms of accuracy and efficiency compared with others. This study's comparison technique is thought to aid breast cancer detection.