Survival-time classification of breast cancer patients

Yuh-Jye Lee*, O. L. Mangasarian, W. H. Wolberg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

The identification of breast cancer patients for whom chemotherapy could prolong survival time is treated here as a data mining problem. This identification is achieved by clustering 253 breast cancer patients into three prognostic groups: Good, Poor and Intermediate. Each of the three groups has a significantly distinct Kaplan-Meier survival curve. Of particular significance is the Intermediate group, because patients with chemotherapy in this group do better than those without chemotherapy in the same group. This is the reverse case to that of the overall population of 253 patients for which patients undergoing chemotherapy have worse survival than those who do not. We also prescribe a procedure that utilizes three nonlinear smooth support vector machines (SSVMs) for classifying breast cancer patients into the three above prognostic groups. These results suggest that the patients in the Good group should not receive chemotherapy while those in the Intermediate group should receive chemotherapy based on our survival curve analysis. To our knowledge this is the first instance of a classifiable group of breast cancer patients for which chemotherapy can possibly enhance survival.

Original languageEnglish
Pages (from-to)151-166
Number of pages16
JournalComputational Optimization and Applications
Volume25
Issue number1-3
DOIs
StatePublished - 1 Apr 2003

Keywords

  • Breast cancer
  • Multicategory classification
  • Support vector machines

Fingerprint Dive into the research topics of 'Survival-time classification of breast cancer patients'. Together they form a unique fingerprint.

Cite this