Comparison of Different Classifiers for Prediction of Breast Cancer Metastasis in Microarray Analysis

Document Type : Original Article (s)

Authors

1 PhD Student, Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

2 Associate Professor, Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: In this research, we investigated the performance of some different classifiers for prediction of metastasis in breast cancer.Methods: We used the DNA microarrays of primary breast tumors of 78 young patients. Among these patients, 34 had developed distant metastases within 5 years (poor prognosis group) and 44 formed good prognosis group. For analysis, we applied three different classifiers including support vector machine (SVM), stepwise linear discriminant analysis (SWLDA) and K-nearest neighbors (KNN) classifier. Each of these classifiers used 231 selected genes as an input feature vector and their performances were estimated via using leave one out (LOO) method to classify patients into two groups namely, good and poor prognosis.Findings: The best results were obtained by support vector machine with linear kernel. This classifier achieved a sensitivity and specificity of 84% and 82%, respectively, for metastasis prediction.Conclusion: Our findings provide a strategy to specify patients who would benefit from adjuvant therapy.

Keywords

References

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Journal of Isfahan Medical School
Volume 32, Issue 292 - Serial Number 292
May and June 2014
Pages 1028-1035

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History

  • Receive Date: 26 October 2013
  • Revise Date: 02 May 2024
  • Accept Date: 06 April 2022

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