Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method

Tongan Cai; Hongliang He; Wenyu Zhang

doi:doi:10.11648/j.acm.20180703.20

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Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method

Tongan Cai, Hongliang He, Wenyu Zhang

Published in Applied and Computational Mathematics (Volume 7, Issue 3)

Received: 2 August 2018 Accepted: Published: 3 August 2018

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Abstract

Worldwide, breast cancer is one of the most threatening killers to mid-aged women. The diagnosis of breast cancer aims to classify spotted breast tumor to be Benign or Malignant. With recent developments in data mining technique, new model structures and algorithms are helping medical workers greatly in improving classification accuracy. In this study, a model is proposed combining ensemble method and imbalanced learning technique for the classification of breast cancer data. First, Synthetic Minority Over-Sampling Technique (SMOTE), an imbalanced learning algorithm is applied to selected datasets and second, multiple baseline classifiers are tuned by Bayesian Optimization. Finally, a stacking ensemble method combines the optimized classifiers for final decision. Comparative analysis shows the proposed model can achieve better performance and adaptivity than conventional methods, in terms of classification accuracy, specificity and AuROC on two mostly-used breast cancer datasets, validating the clinical value of this model.

Published in	Applied and Computational Mathematics (Volume 7, Issue 3)
DOI	10.11648/j.acm.20180703.20
Page(s)	146-154
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Mining, Breast Cancer, Ensemble Method, Imbalanced Learning

References

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APA Style

Tongan Cai, Hongliang He, Wenyu Zhang. (2018). Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method. Applied and Computational Mathematics, 7(3), 146-154. https://doi.org/10.11648/j.acm.20180703.20

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ACS Style

Tongan Cai; Hongliang He; Wenyu Zhang. Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method. Appl. Comput. Math. 2018, 7(3), 146-154. doi: 10.11648/j.acm.20180703.20

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AMA Style

Tongan Cai, Hongliang He, Wenyu Zhang. Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method. Appl Comput Math. 2018;7(3):146-154. doi: 10.11648/j.acm.20180703.20

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@article{10.11648/j.acm.20180703.20,
  author = {Tongan Cai and Hongliang He and Wenyu Zhang},
  title = {Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method},
  journal = {Applied and Computational Mathematics},
  volume = {7},
  number = {3},
  pages = {146-154},
  doi = {10.11648/j.acm.20180703.20},
  url = {https://doi.org/10.11648/j.acm.20180703.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20180703.20},
  abstract = {Worldwide, breast cancer is one of the most threatening killers to mid-aged women. The diagnosis of breast cancer aims to classify spotted breast tumor to be Benign or Malignant. With recent developments in data mining technique, new model structures and algorithms are helping medical workers greatly in improving classification accuracy. In this study, a model is proposed combining ensemble method and imbalanced learning technique for the classification of breast cancer data. First, Synthetic Minority Over-Sampling Technique (SMOTE), an imbalanced learning algorithm is applied to selected datasets and second, multiple baseline classifiers are tuned by Bayesian Optimization. Finally, a stacking ensemble method combines the optimized classifiers for final decision. Comparative analysis shows the proposed model can achieve better performance and adaptivity than conventional methods, in terms of classification accuracy, specificity and AuROC on two mostly-used breast cancer datasets, validating the clinical value of this model.},
 year = {2018}
}

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TY  - JOUR
T1  - Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method
AU  - Tongan Cai
AU  - Hongliang He
AU  - Wenyu Zhang
Y1  - 2018/08/03
PY  - 2018
N1  - https://doi.org/10.11648/j.acm.20180703.20
DO  - 10.11648/j.acm.20180703.20
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 146
EP  - 154
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20180703.20
AB  - Worldwide, breast cancer is one of the most threatening killers to mid-aged women. The diagnosis of breast cancer aims to classify spotted breast tumor to be Benign or Malignant. With recent developments in data mining technique, new model structures and algorithms are helping medical workers greatly in improving classification accuracy. In this study, a model is proposed combining ensemble method and imbalanced learning technique for the classification of breast cancer data. First, Synthetic Minority Over-Sampling Technique (SMOTE), an imbalanced learning algorithm is applied to selected datasets and second, multiple baseline classifiers are tuned by Bayesian Optimization. Finally, a stacking ensemble method combines the optimized classifiers for final decision. Comparative analysis shows the proposed model can achieve better performance and adaptivity than conventional methods, in terms of classification accuracy, specificity and AuROC on two mostly-used breast cancer datasets, validating the clinical value of this model.
VL  - 7
IS  - 3
ER  -

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Author Information

Tongan Cai

Department of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, USA
Hongliang He

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

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APA Style

Tongan Cai, Hongliang He, Wenyu Zhang. (2018). Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method. Applied and Computational Mathematics, 7(3), 146-154. https://doi.org/10.11648/j.acm.20180703.20

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ACS Style

Tongan Cai; Hongliang He; Wenyu Zhang. Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method. Appl. Comput. Math. 2018, 7(3), 146-154. doi: 10.11648/j.acm.20180703.20

Copy | Download

AMA Style

Tongan Cai, Hongliang He, Wenyu Zhang. Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method. Appl Comput Math. 2018;7(3):146-154. doi: 10.11648/j.acm.20180703.20

Copy | Download

@article{10.11648/j.acm.20180703.20,
  author = {Tongan Cai and Hongliang He and Wenyu Zhang},
  title = {Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method},
  journal = {Applied and Computational Mathematics},
  volume = {7},
  number = {3},
  pages = {146-154},
  doi = {10.11648/j.acm.20180703.20},
  url = {https://doi.org/10.11648/j.acm.20180703.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20180703.20},
  abstract = {Worldwide, breast cancer is one of the most threatening killers to mid-aged women. The diagnosis of breast cancer aims to classify spotted breast tumor to be Benign or Malignant. With recent developments in data mining technique, new model structures and algorithms are helping medical workers greatly in improving classification accuracy. In this study, a model is proposed combining ensemble method and imbalanced learning technique for the classification of breast cancer data. First, Synthetic Minority Over-Sampling Technique (SMOTE), an imbalanced learning algorithm is applied to selected datasets and second, multiple baseline classifiers are tuned by Bayesian Optimization. Finally, a stacking ensemble method combines the optimized classifiers for final decision. Comparative analysis shows the proposed model can achieve better performance and adaptivity than conventional methods, in terms of classification accuracy, specificity and AuROC on two mostly-used breast cancer datasets, validating the clinical value of this model.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method
AU  - Tongan Cai
AU  - Hongliang He
AU  - Wenyu Zhang
Y1  - 2018/08/03
PY  - 2018
N1  - https://doi.org/10.11648/j.acm.20180703.20
DO  - 10.11648/j.acm.20180703.20
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 146
EP  - 154
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20180703.20
AB  - Worldwide, breast cancer is one of the most threatening killers to mid-aged women. The diagnosis of breast cancer aims to classify spotted breast tumor to be Benign or Malignant. With recent developments in data mining technique, new model structures and algorithms are helping medical workers greatly in improving classification accuracy. In this study, a model is proposed combining ensemble method and imbalanced learning technique for the classification of breast cancer data. First, Synthetic Minority Over-Sampling Technique (SMOTE), an imbalanced learning algorithm is applied to selected datasets and second, multiple baseline classifiers are tuned by Bayesian Optimization. Finally, a stacking ensemble method combines the optimized classifiers for final decision. Comparative analysis shows the proposed model can achieve better performance and adaptivity than conventional methods, in terms of classification accuracy, specificity and AuROC on two mostly-used breast cancer datasets, validating the clinical value of this model.
VL  - 7
IS  - 3
ER  -

Copy | Download