Nomographic representation of logistic regression models: a case study using patient self-assessment data

Authors:
Stephan Dreiseitl;Alexandra Harbauer;Michael Binder;Harald Kittler
Affiliations:
Department of Software Engineering, University of Applied Sciences Upper Austria at Hagenberg, Austria;Department of Dermatology, Medical University of Vienna, Austria;Department of Dermatology, Medical University of Vienna, Austria;Department of Dermatology, Medical University of Vienna, Austria
Venue:
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Year:
2005

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Artificial Intelligence in Medicine

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Abstract

Logistic regression models are widely used in medicine, but difficult to apply without the aid of electronic devices. In this paper, we present a novel approach to represent logistic regression models as nomograms that can be evaluated by simple line drawings. As a case study, we show how data obtained from a questionnaire-based patient self-assessment study on the risks of developing melanoma can be used to first identify a subset of significant covariates, build a logistic regression model, and finally transform the model to a graphical format. The advantage of the nomogram is that it can easily be mass-produced, distributed and evaluated, while providing the same information as the logistic regression model it represents.