|
KMID : 1155220180430020140
|
|
Journal of the Korean Society of Health Information and Health Statistics
2018 Volume.43 No. 2 p.140 ~ p.147
|
|
|
Breast Cancer Classification using Deep Learning-based Ensemble
|
|
Choi Do-Yeon
Jeong Kwang-Mo
Lim Dong-Hoon
|
|
|
Abstract
|
|
|
Objectives: We propose a deep learning-based ensemble for improving breast cancer classification and compare it with existing six models including deep neural network on two UCI data.
Methods: We propose a deep learning-based stacking ensemble method. We first applied five classifications methods individually, which were k-nearest neighbor, decision trees, support vector machines, discriminant analysis, and logistic regression analysis and then adopt a deep learning to the predictions derived from these methods after using 5-fold cross validation technique. We compared the proposed deep learning-based ensemble method with these methods for two UCI data through classification accuracy, ROC curves and c-statistics.
Results: Exper?imental results for two UCI data showed that the proposed deep learning-based ensemble outperformed single k-nearest neighbor, decision trees, sup?port vector machines discriminant analysis, and logistic regression analysis as well as deep neural network in terms of various performance measures.
Conclusions: We proposed deep learning-based ensemble for improving breast cancer classification. The deep learning-based ensemble outperformed existing single models for all applications in terms of various performance measures.
|
|
|
KEYWORD
|
|
|
Breast cancer, Classification, Deep learning, Ensemble, Performance evaluation
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
|
|