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Validation procedures in radiological diagnostic models. Neural network and logistic regression
Arana, Estanislao; Delicado, Pedro; Martí, Luis
Universitat Pompeu Fabra. Departament d'Economia i Empresa
The objective of this paper is to compare the performance of twopredictive 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.
2005-09-15
Statistics, Econometrics and Quantitative Methods
skull
neoplasms
logistic regression
neural networks
receiver operating characteristic curve
statistics
resampling
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Working Paper
         

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