Analysis of Machine Learning Algorithms on Cancer Dataset

Abstract

The most promising of all cancers that are prevailing among and the primary source of women's deaths worldwide is the cancerous breast cells. Accurate discovery of this type of cancer cells is essential in its early stages, which can be attained via. various data mining and machine learning techniques. Therefore, a comparative analysis among different machine learning techniques such as Random Forest, Support Vector Machine, Naive Bayes, Decision Tree, Neural Networks and Logistic Regression is conducted. It is determined using the WEKA tool. Also, the selected machine learning algorithms are evaluated based on accuracy in prediction results and performance comparison of each classifier with a ROC curve on multiple classifiers is performed.