On the performance of ensemble learning for automated diagnosis of breast cancer

Abstract

The automated diagnosis of diseases with high accuracy rate is one of the most crucial problems in medical informatics. Machine learning algorithms are widely utilized for automatic detection of illnesses. Breast cancer is one of the most common cancer types in females and the second most common cause of death from cancer in females. Hence, developing an efficient classifier for automated diagnosis of breast cancer is essential to improve the chance of diagnosing the disease at the earlier stages and treating it more properly. Ensemble learning is a branch of machine learning that seeks to use multiple learning algorithms so that better predictive performance acquired. Ensemble learning is a promising field for improving the performance of base classifiers. This paper is concerned with the comparative assessment of the performance of six popular ensemble methods (Bagging, Dagging, Ada Boost, Multi Boost, Decorate, and Random Subspace) based on fourteen base learners (Bayes Net, FURIA, Knearest Neighbors, C4.5, RIPPER, Kernel Logistic Regression, K-star, Logistic Regression, Multilayer Perceptron, Naïve Bayes, Random Forest, Simple Cart, Support Vector Machine, and LMT) for automatic detection of breast cancer. The empirical results indicate that ensemble learning can improve the predictive performance of base learners on medical domain. The best results for comparative experiments are acquired with Random Subspace ensemble method. The experiments show that ensemble learning methods are appropriate methods to improve the performance of classifiers for medical diagnosis.