On the Performance of Ensemble Learning for Automated Diagnosis of Breast Cancer

Abstract

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.

A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

“…Ensemble approaches to machine learning in cancer diagnosis were also investigated by Onan [58]. Six ensemble methods were studied (Bagging, Dagging, Ada Boost, Multi Boost, Decorate, and Random Subspace) within 14 ML algorithms for automatic detection of breast cancer using features computed from digitized images of fine needle aspirate (FNA) of a breast mass.…”

Pixel-Wise Classification in Hippocampus Histological Images

“…There are algorithms used in ML that have been proven to maximize predictive output such as ensemble learning [45]; however, this work is constrained to the mentioned ML models with the purpose of evaluating the pixel characterization method itself.…”

Random Forest Classifier for Diagnosis of Breast Cancer in African Women

“…Cancer belongs to a class of diseases that result from abnormal cell growth [6]. Diagnosis and treatment of breast cancer in its earliest stage remains the only way to improve its outcome and reduce mortality, thus early and accurate diagnosis of breast cancer is important [7,8]. To survive breast cancer in the long term, metastasis of the cancerous cell must be halted through appropriate medical intervention at the early stage [7].…”

“…Diagnosis and treatment of breast cancer in its earliest stage remains the only way to improve its outcome and reduce mortality, thus early and accurate diagnosis of breast cancer is important [7,8]. To survive breast cancer in the long term, metastasis of the cancerous cell must be halted through appropriate medical intervention at the early stage [7]. Early detection of breast cancer among women in Sub-Saharan Africa (SSA) is very challenging because of lack of awareness, treatment cost [9,10].…”