Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes.

Journal: Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
PMID: 30851764

Abstract

OBJECTIVE: Support vector machines (SVM) is a newer statistical method that has been reported to be advantageous to traditional logistic regression for clinical classification. We determine if SVM can better predict the results of temporal artery biopsy (TABx) for giant cell arteritis compared to logistic regression.

Authors

  • Edsel Ing
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ont.; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ont.. Electronic address: edingLidStrab@gmail.com.
  • Wanhua Su
    Department of Mathematics and Statistics, MacEwan University, Edmonton, Alta.
  • Matthias Schonlau
    Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ont.
  • Nurhan Torun
    Department of Ophthalmology, Harvard Medical School, Boston, MA, United States.; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ont.

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