Naive Bayesian-based nomogram for prediction of prostate cancer recurrence.
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.
Demsar
J
J
Faculty of Computer Science, University of Ljubljana, Slovenia.
Zupan
B
B
Kattan
M W
MW
Beck
J R
JR
Bratko
I
I
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