Validation procedures in radiologic diagnostic models. Neural network and logistic regression.
OBJECTIVE
To compare the performance of two predictive radiologic models, logistic regression (LR) and neural network (NN), with five different resampling methods.


METHODS
One hundred 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).


RESULTS
The NN obtained statistically higher Az values than LR with cross-validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules.


CONCLUSIONS
The NN classifier performs better than the one based on LR. This advantage is well detected by three-fold cross-validation but remains unnoticed when leave-one-out or bootstrap algorithms are used.
Arana
E
E
Department of Radiology, Hospital Casa de Salud, Valencia, Spain.
Delicado
P
P
Martí-Bonmatí
L
L
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012888:Skull Neoplasms / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed