Comparative Study of Different Machine Learning Classifiers Using Multiple Feature Selection Techniques for Breast Cancer Classification

Abstract

Abstract: This research investigates use of several Machine Learning classifiers under feature selection methods: Without Dimensionality reduction, using Correlation Coefficient Score, using Voting Classifier, and using Tree Based Feature Selection. The different ML Classifiers used in this research are: Logistic Regression, Decision Trees, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbours (KNN) and Naïve Bayes Classifier. These classification models are run on data generated from processing mammography scans to extract shape, texture, size and other spatial features from the tumour contour. The performance of these ML classifiers is evaluated by performance metrics like: Precision Score, Recall Score, F1 Score, and Accuracy Score. The dataset used for the purpose of our study was The Wisconsin Breast Cancer Dataset for both training and testing. The comparison of these results helps us better understand the nature of these classifiers for such classification problems, give us more insights on feature engineering and selection, and their potential use in clinical trials. After computing the results, we were able to get accuracy levels as high as 97.9% and were able to reach accuracy between 90- 95% in general.