Paper #414
- Título:
- Validation procedures in radiological diagnostic models. Neural network and logistic regression
- Autores:
- Estanislao Arana, Pedro Delicado y Luis MartÃ
- Data:
- Octubre 1999
- Resumen:
- 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.
- Palabras clave:
- Skull, neoplasms, logistic regression, neural networks, receiver operating characteristic curve, statistics, resampling
- Códigos JEL:
- C13, C14
- Área de investigación:
- Estadística, Econometría y Métodos Cuantitativos
- Publicado en:
- Investigative Radiology, 34, 636-642, 1999
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