Validation procedures in radiological diagnostic models. Neural network and logistic regression

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• dc.contributor.author Arana, Estanislaoca
• dc.contributor.author Delicado, Pedroca
• dc.contributor.author Martí, Luisca
• dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
• dc.date.accessioned 2017-07-26T10:50:12Z
• dc.date.available 2017-07-26T10:50:12Z
• dc.date.issued 1999-10-01
• dc.date.modified 2017-07-23T02:04:49Z
• dc.description.abstract The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.
• dc.format.mimetype application/pdfca
• dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=414
• dc.identifier.citation Investigative Radiology, 34, 636-642, 1999
• dc.identifier.uri http://hdl.handle.net/10230/1125
• dc.language.iso eng
• dc.relation.ispartofseries Economics and Business Working Papers Series; 414
• dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
• dc.subject.keyword skull
• dc.subject.keyword neoplasms
• dc.subject.keyword logistic regression
• dc.subject.keyword neural networks
• dc.subject.keyword receiver operating characteristic curve
• dc.subject.keyword statistics
• dc.subject.keyword resampling
• dc.subject.keyword Statistics, Econometrics and Quantitative Methods
• dc.title Validation procedures in radiological diagnostic models. Neural network and logistic regressionca
• dc.type info:eu-repo/semantics/workingPaper