Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

Authors:
Sheau-Ling Hsieh;Sung-Huai Hsieh;Po-Hsun Cheng;Chi-Huang Chen;Kai-Ping Hsu;I-Shun Lee;Zhenyu Wang;Feipei Lai
Affiliations:
Network and Computer Centre, National Chiao Tung University, Hsinchu, Taiwan;Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan;Department of Software Engineering, National Kaohsiung Normal University, Kaohsiung, Taiwan;Network and Computer Centre, National Taiwan University, Taipei, Taiwan;Network and Computer Centre, National Taiwan University, Taipei, Taiwan;Network and Computer Centre, National Chiao Tung University, Hsinchu, Taiwan;Computing Laboratory, Oxford University, Oxford, UK;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan and Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan and Grad ...
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.