Literature DB >> 12767148

Diagnosing breast cancer based on support vector machines.

H X Liu1, R S Zhang, F Luan, X J Yao, M C Liu, Z D Hu, B T Fan.   

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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Year:  2003        PMID: 12767148     DOI: 10.1021/ci0256438

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  14 in total

1.  QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.

Authors:  H X Liu; R S Zhang; X J Yao; M C Liu; Z D Hu; B T Fan
Journal:  J Comput Aided Mol Des       Date:  2004-06       Impact factor: 3.686

2.  The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine.

Authors:  H X Liu; R J Hu; R S Zhang; X J Yao; M C Liu; Z D Hu; B T Fan
Journal:  J Comput Aided Mol Des       Date:  2005-01       Impact factor: 3.686

Review 3.  Molecular similarity and diversity in chemoinformatics: from theory to applications.

Authors:  Ana G Maldonado; J P Doucet; Michel Petitjean; Bo-Tao Fan
Journal:  Mol Divers       Date:  2006-02       Impact factor: 2.943

4.  Are there any differences between features of proteins expressed in malignant and benign breast cancers?

Authors:  Mansour Ebrahimi; Esmaeil Ebrahimie; Narges Shamabadi; Mahdi Ebrahimi
Journal:  J Res Med Sci       Date:  2010-11       Impact factor: 1.852

5.  Average rank-based score to measure deregulation of molecular pathway gene sets.

Authors:  Huan Yang; Chao Cheng; Wei Zhang
Journal:  PLoS One       Date:  2011-11-09       Impact factor: 3.240

6.  A novel weighted support vector machine based on particle swarm optimization for gene selection and tumor classification.

Authors:  Mohammad Javad Abdi; Seyed Mohammad Hosseini; Mansoor Rezghi
Journal:  Comput Math Methods Med       Date:  2012-07-26       Impact factor: 2.238

7.  Multiple biomarker panels for early detection of breast cancer in peripheral blood.

Authors:  Fan Zhang; Youping Deng; Renee Drabier
Journal:  Biomed Res Int       Date:  2013-11-26       Impact factor: 3.411

8.  Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.

Authors:  Tae Keun Yoo; Sung Kean Kim; Deok Won Kim; Joon Yul Choi; Wan Hyung Lee; Ein Oh; Eun-Cheol Park
Journal:  Yonsei Med J       Date:  2013-11       Impact factor: 2.759

9.  Predicting metabolic syndrome using decision tree and support vector machine methods.

Authors:  Farzaneh Karimi-Alavijeh; Saeed Jalili; Masoumeh Sadeghi
Journal:  ARYA Atheroscler       Date:  2016-05

Review 10.  Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

Authors:  Afsaneh Jalalian; Syamsiah Mashohor; Rozi Mahmud; Babak Karasfi; M Iqbal B Saripan; Abdul Rahman B Ramli
Journal:  EXCLI J       Date:  2017-02-20       Impact factor: 4.068

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