Diagnosing breast cancer based on support vector machines

H. X. Liu; R. S. Zhang; F. Luan; X. J. Yao; M. C. Liu; Z. D. Hu; B. T. Fan

doi:10.1021/ci0256438

Diagnosing breast cancer based on support vector machines

H. X. Liu, R. S. Zhang, F. Luan, X. J. Yao, M. C. Liu, Z. D. Hu, B. T. Fan

Research output: Contribution to journal › Article › peer-review

119 Citations (Scopus)

Abstract

The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.

Original language	English
Pages (from-to)	900-907
Number of pages	8
Journal	Journal of Chemical Information and Computer Sciences
Volume	43
Issue number	3
DOIs	https://doi.org/10.1021/ci0256438
Publication status	Published - May 2003
Externally published	Yes

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10.1021/ci0256438

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abstract = "The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.",

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Diagnosing breast cancer based on support vector machines

Abstract

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