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Spatial modeling and classification of corneal shape.

Publication ,  Journal Article
Marsolo, K; Twa, M; Bullimore, MA; Parthasarathy, S
Published in: IEEE Trans Inf Technol Biomed
March 2007

One of the most promising applications of data mining is in biomedical data used in patient diagnosis. Any method of data analysis intended to support the clinical decision-making process should meet several criteria: it should capture clinically relevant features, be computationally feasible, and provide easily interpretable results. In an initial study, we examined the feasibility of using Zernike polynomials to represent biomedical instrument data in conjunction with a decision tree classifier to distinguish between the diseased and non-diseased eyes. Here, we provide a comprehensive follow-up to that work, examining a second representation, pseudo-Zernike polynomials, to determine whether they provide any increase in classification accuracy. We compare the fidelity of both methods using residual root-mean-square (rms) error and evaluate accuracy using several classifiers: neural networks, C4.5 decision trees, Voting Feature Intervals, and Naïve Bayes. We also examine the effect of several meta-learning strategies: boosting, bagging, and Random Forests (RFs). We present results comparing accuracy as it relates to dataset and transformation resolution over a larger, more challenging, multi-class dataset. They show that classification accuracy is similar for both data transformations, but differs by classifier. We find that the Zernike polynomials provide better feature representation than the pseudo-Zernikes and that the decision trees yield the best balance of classification accuracy and interpretability.

Duke Scholars

Published In

IEEE Trans Inf Technol Biomed

DOI

ISSN

1089-7771

Publication Date

March 2007

Volume

11

Issue

2

Start / End Page

203 / 212

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Models, Biological
  • Medical Informatics
  • Humans
  • Feasibility Studies
  • Diagnosis, Computer-Assisted
  • Corneal Topography
  • Cornea
 

Citation

APA
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ICMJE
MLA
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Marsolo, K., Twa, M., Bullimore, M. A., & Parthasarathy, S. (2007). Spatial modeling and classification of corneal shape. IEEE Trans Inf Technol Biomed, 11(2), 203–212. https://doi.org/10.1109/titb.2006.879591
Marsolo, Keith, Michael Twa, Mark A. Bullimore, and Srinivasan Parthasarathy. “Spatial modeling and classification of corneal shape.IEEE Trans Inf Technol Biomed 11, no. 2 (March 2007): 203–12. https://doi.org/10.1109/titb.2006.879591.
Marsolo K, Twa M, Bullimore MA, Parthasarathy S. Spatial modeling and classification of corneal shape. IEEE Trans Inf Technol Biomed. 2007 Mar;11(2):203–12.
Marsolo, Keith, et al. “Spatial modeling and classification of corneal shape.IEEE Trans Inf Technol Biomed, vol. 11, no. 2, Mar. 2007, pp. 203–12. Pubmed, doi:10.1109/titb.2006.879591.
Marsolo K, Twa M, Bullimore MA, Parthasarathy S. Spatial modeling and classification of corneal shape. IEEE Trans Inf Technol Biomed. 2007 Mar;11(2):203–212.

Published In

IEEE Trans Inf Technol Biomed

DOI

ISSN

1089-7771

Publication Date

March 2007

Volume

11

Issue

2

Start / End Page

203 / 212

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Models, Biological
  • Medical Informatics
  • Humans
  • Feasibility Studies
  • Diagnosis, Computer-Assisted
  • Corneal Topography
  • Cornea