An Evaluation of the Wisconsin Breast Cancer Dataset using Ensemble Classifiers and RFE Feature Selection Technique
Keywords:
Breast Cancer, WBCD, XGBoost, RF, RFE, Ensemble ClassifiersAbstract
Breast cancer represents one of the deadliest diseases that records a high number of death rate annually. It is the most common type of cancer and the main cause of death among women worldwide. Machine learning (ML) approach is an effective way to classify data, especially in medical field. It is widely used for classification and analysis to make decisions. In this paper, a performance comparison between two ensemble ML classifiers: Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) on the Wisconsin Breast Cancer Dataset (WBCD) is conducted. The main objective of this study is to assess the correctness of the classifiers with respect to their efficiency and effectiveness in classifying the dataset. This was done by utilizing all and reduced features of the dataset that were generated with Recursive Feature Elimination (RFE) feature selection technique. Four metrics were used in the study: Accuracy, Precision, Recall and F1-Score to evaluate the classifiers. All experiments were executed within Anaconda Environment with Jupyter Notebook and conducted using Python programming language. Experimental result shows that XGBoost with 5 reduced feature using RFE feature selection technique gives the highest accuracy (99.02%) with lowest error rate.
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