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

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  • Estanislao Arana
  • Pedro Delicado
  • Luis Martí

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.

Suggested Citation

  • Estanislao Arana & Pedro Delicado & Luis Martí, 1999. "Validation procedures in radiological diagnostic models. Neural network and logistic regression," Economics Working Papers 414, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:414
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    References listed on IDEAS

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    1. Houshmand A. Ziari & David J. Leatham & Paul N. Ellinger, 1997. "Development of Statistical Discriminant Mathematical Programming Model Via Resampling Estimation Techniques," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(4), pages 1352-1362.
    2. Weigend, Andreas S. & Lebaron, Blake, 1994. "Evaluating Neural Network Predictors by Bootstrapping," SFB 373 Discussion Papers 1994,35, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    Cited by:

    1. Bergtold, Jason S. & Taylor, Daniel B. & Bosch, Darrell J., 2003. "Networking Your Way to a Better Prediction: Effectively Modeling Contingent Valuation Survey Data," 2003 Annual meeting, July 27-30, Montreal, Canada 22152, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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    More about this item

    Keywords

    Skull; neoplasms; logistic regression; neural networks; receiver operating characteristic curve; statistics; resampling;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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