Breast Cancer in Python

 Abstract

Breast cancer is a dominant cancer in women worldwide and is increasing in developing countries where the majority of cases are diagnosed in late stages. The projects that have already been proposed show a comparison of machine learning algorithms with the help of different techniques like the ensemble methods, data mining algorithm sourcing blood analysis etc. This paper proposed now presents a comparison of six machine learning(ML) algorithms: Naïve Bayes(NB), Random Forest(RT), Artificial Neural Networks(ANN), Nearest Neighbour(KNN), Support Vector Machine(SVM) and Decision Tree(DT)on the Wiscons in Diagnostic Breast Cancer(WDBC) dataset which is extracted from a digitized image of an MRI. For the implementation of the ML algorithms, the dataset was partitioned into the training phase and the testing phase. The algorithm with the best results will be used as the back end to the website and the model will then classify the cancer as benignormalignant.