Abstract
Automated classification systems are often used for patient diagnosis. In many cases, the rationale behind a decision is as important as the decision itself. Here we detail a method of visualizing the criteria used by a decision tree classifier to provide support for clinicians interested in diagnosing corneal disease. We leverage properties of our data transformation to create surfaces highlighting the details deemed important in classification. Preliminary results indicate that the features illustrated by our visualization method are indeed the criteria that often lead to a correct diagnosis and that our system also seems to find favor with practicing clinicians.
This work was partially supported by the following grants: NIH-NEI T32-EY13359, NIH-NEI K23-EY16225 and American Optometric Foundation William Ezell Fellowship, Ocular Sciences Inc. (MT); NSF Career Grant IIS-0347662 (KM, SP); NIH-NEI R01-EY12952 (MB).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Thibos, L., Applegate, R., Schwiegerling, J.T., Webb, R.: Standards for reporting the optical aberrations of eyes. In: TOPS, Santa Fe, NM, OSA (1999)
Iskander, D., Collins, M., Davis, B.: Optimal modeling of corneal surfaces with zernike polynomials. IEEE Trans Biomed Eng 48, 87–95 (2001)
Iskander, D.R., Collins, M.J., Davis, B., Franklin, R.: Corneal surface characterization: How many zernike terms should be used (arvo abstract). Invest Ophthalmol Vis Sci 42, S896 (2001)
Twa, M., Parthasarathy, S., Raasch, T., Bullimore, M.: Automated classification of keratoconus: A case study in analyzing clinical data. In: SIAM Intl. Conference on Data Mining, San Francisco, CA (2003)
Maeda, N., Klyce, S.D., Smolek, M.K., Thompson, H.W.: Automated keratoconus screening with corneal topography analysis. Invest Ophthalmol Vis Sci 35, 2749–2757 (1994)
Rabinowitz, Y.S., Rasheed, K.: Kisaminimal topographic criteria for diagnosing keratoconus. J Cataract Refract Surg 25, 1327–1335 (1999)
Smolek, M.K., Klyce, S.D.: Screening of prior refractive surgery by a wavelet-based neural network. J Cataract Refract Surg 27, 1926–1931 (2001)
Maeda, N., Klyce, S.D., Smolek, M.K.: Neural network classification of corneal topography. preliminary demonstration. Invest Ophthalmol Vis Sci 36, 1327–1335 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marsolo, K., Parthasarathy, S., Twa, M., Bullimore, M. (2005). A Model-Based Approach to Visualizing Classification Decisions for Patient Diagnosis. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_64
Download citation
DOI: https://doi.org/10.1007/11527770_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
eBook Packages: Computer ScienceComputer Science (R0)