Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine

Woojae Kim; Ku Sang Kim; Jeong Eon Lee; Dong‐Young Noh; Sung-Won Kim; Yong Sik Jung; Man Young Park; Rae Woong Park

doi:10.4048/jbc.2012.15.2.230

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Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine

Woojae Kim Ku Sang Kim Jeong Eon Lee Dong‐Young Noh Sung-Won Kim Yong Sik Jung Man Young Park Rae Woong Park

Year: 2012 Journal: Journal of Breast Cancer Vol: 15 (2)Pages: 230-230

DOI: 10.4048/jbc.2012.15.2.230

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Abstract

As the selected prognostic factors can be easily obtained in clinical practice, the proposed model might prove useful in the prediction of breast cancer recurrence. The prediction model is freely available in the website (http://ami.ajou.ac.kr/bcr/).

Keywords:

Medicine Breast cancer Lymphovascular invasion Proportional hazards model Oncology Internal medicine Univariate Support vector machine Artificial intelligence Lymph node Adjuvant therapy Cancer Machine learning Multivariate statistics Metastasis Computer science

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Topics

Breast Cancer Treatment Studies

Life Sciences → Biochemistry, Genetics and Molecular Biology → Cancer Research

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine

Abstract

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Citation History

Topics

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