title: Naive Bayesian-based nomogram for prediction of prostate cancer recurrence.
abstract: This paper introduces a schema with naive-Bayesian classifier and patient weighting technique to develop a prostate cancer recurrence prediction model from patient data. We propose the graphical presentation of naive-Bayesian classifier with a nomogram, which can be used both for prediction or can provide means to data analysis. The resulting model was experimentally evaluated; the results were favorable both in terms of interpretability and predictive accuracy.
authors.affiliation: Faculty of Computer Science, University of Ljubljana, Slovenia.
authors.forename: J
authors.initials: J
authors.lastname: Demsar
authors.forename: B
authors.initials: B
authors.lastname: Zupan
authors.forename: M W
authors.initials: MW
authors.lastname: Kattan
authors.forename: J R
authors.initials: JR
authors.lastname: Beck
authors.forename: I
authors.initials: I
authors.lastname: Bratko
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008297:Male; D008432:Mathematical Computing; D009364:Neoplasm Recurrence, Local / Q000453:epidemiology* / Q000473:pathology; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D011336:Probability; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000453:epidemiology* / Q000473:pathology; D016019:Survival Analysis