A new nested ensemble technique for automated diagnosis of breast cancer

Abdar, Moloud and Zomorodi-Moghadam, Mariam and Zhou, Xujuan and Gururajan, Raj and Tao, Xiaohui and Barua, Prabal D. and Gururajan, Rashmi (2018) A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recognition Letters. ISSN 0167-8655


Nowadays, breast cancer is reported as one of most common cancers amongst women. Early detection of this cancer is an essential to aid in informing subsequent treatments. This study investigates automated breast cancer prediction using machine learning and data mining techniques. We proposed the nested ensemble approach which used the Stacking and Vote (Voting) as the classifiers combination techniques in our ensemble methods for detecting the benign breast tumors from malignant cancers. Each nested ensemble classifier contains 'Classifiers' and 'MetaClassifiers'. MetaClassifiers can have more than two different classification algorithms. In this research, we developed the two-layer nested ensemble classifiers. In our two-layer nested ensemble classifiers the MetaClassifiers have two or three different classification algorithms. We conducted the experiments on Wisconsin Diagnostic Breast Cancer (WDBC) dataset and K-fold Cross Validation technique are used for the model evaluation. We compared the proposed two-layer nested ensemble classifiers with single classifiers (i.e., BayesNet and Naive Bayes) in terms of the classification accuracy, precision, recall, F1 measure, ROC and computational times of training single and nested ensemble classifiers. We also compared our best model with previous works reported in the literatures in terms of accuracy. The results demonstrate that the proposed two-layer nested ensemble models outperformance the single classifiers and most of the previous works. Both SV-BayesNet-3-MetaClassifier and SV-Naive Bayes-3-MetaClassifier
achieved accuracy 98.07% (K = 10). However, SV-Naive Bayes-3-MetaClassifier is more efficiency as it needs less time to build the model.

Statistics for USQ ePrint 35379
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 27 Dec 2018. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 26 Feb 2019 06:44
Last Modified: 28 Feb 2019 04:18
Uncontrolled Keywords: BayesNet classifier, breast cancer, data mining and machine learning, Naive Bayes classifier, nested ensemble technique
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Funding Details:
Identification Number or DOI: 10.1016/j.patrec.2018.11.004
URI: http://eprints.usq.edu.au/id/eprint/35379

Actions (login required)

View Item Archive Repository Staff Only