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Advances in Dental Research
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Adv Dent Res 17:109-114, December, 2003
© 2003 SAGE Publications

Dental Data Mining: Potential Pitfalls and Practical Issues

Presented at "Dental Informatics & Dental Research: Making the Connection", a conference held in, Bethesda, MD, USA, June 12–13, 2003, sponsored by the University of Pittsburgh Center for Dental Informatics and supported in part by award 1R13DE014611-01 from the National Institute of Dental and Craniofacial Research/National Library of Medicine.

S.A. Gansky

Center for Health and Community, Center to Address Disparities in Children’s Oral Health, Department of Preventive and Restorative Dental Sciences, Division of Oral Epidemiology and Dental Public Health, University of California, San Francisco, CA 94143-1361, USA; sgansky{at}itsa.ucsf.edu

Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports (e.g., Kattan et al., 1998) show that ANN and CART models can perform better than classic regression models: CART models excel at covariate interactions, while ANN models excel at nonlinear covariates. Model prediction performance is examined with the use of validation procedures and evaluating concordance, sensitivity, specificity, and likelihood ratio. To aid interpretation, various plots of predicted probabilities are utilized, such as lift charts, receiver operating characteristic curves, and cumulative captured-response plots. A dental caries study is used as an illustrative example. This paper compares the performance of logistic regression with KDD methods of CART and ANN in analyzing data from the Rochester caries study. With careful analysis, such as validation with sufficient sample size and the use of proper competitors, problems of naïve KDD analyses (Schwarzer et al., 2000) can be carefully avoided.

Key Words: Models, statistical • decision support techniques • neural networks (computer) • dental caries • oral health

Advances in Dental Research, Vol. 17, No. 1, 109-114 (2003)
DOI: 10.1177/154407370301700125
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