title: Nomographic representation of logistic regression models: a case study using patient self-assessment data.
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.
authors.affiliation: Department of Software Engineering, University of Applied Sciences, Upper Austria at Hagenberg, Austria. Stephan.Dreiseitl@fh-hagenberg.at
authors.forename: Stephan
authors.initials: S
authors.lastname: Dreiseitl
authors.forename: Alexandra
authors.initials: A
authors.lastname: Harbauer
authors.forename: Michael
authors.initials: M
authors.lastname: Binder
authors.forename: Harald
authors.initials: H
authors.lastname: Kittler
mesh_terms: D000465:Algorithms; D001317:Austria / Q000453:epidemiology; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D015994:Incidence; D008545:Melanoma / Q000175:diagnosis* / Q000453:epidemiology*; D009716:Numerical Analysis, Computer-Assisted; D011379:Prognosis; D012372:ROC Curve; D012044:Regression Analysis; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D016353:Self-Examination / Q000379:methods*