156580-Thumbnail Image.png
Description
This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide

This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of the temperature domain that are critical for population discrimination. The undertaken analyses suggest that derivate-based information contributes significantly in improved classification performance relative to recently published studies on SLE plasma thermograms.


Download restricted.

Details

Title
  • Supervised and ensemble classification of multivariate functional data: applications to lupus diagnosis
Contributors
Date Created
2018
Resource Type
  • Text
  • Collections this item is in
    Note
    • thesis
      Partial requirement for: Ph.D., Arizona State University, 2018
    • bibliography
      Includes bibliographical references (pages 180-187)
    • Field of study: Applied mathematics

    Citation and reuse

    Statement of Responsibility

    by Robert Buscaglia

    Machine-readable links