• Teshnizi Saeed Hosseini SH Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran. and Sayyed Mohhamad Taghi Ayatollahi.
• Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran.
• Acta Inform Med. 2015 Oct 1; 23 (5): 296-300.

Background And ObjectiveArtificial Neural Networks (ANNs) have recently been applied in situations where an analysis based on the logistic regression (LR) is a standard statistical approach; direct comparisons of the results, however, are seldom attempted. In this study, we compared both logistic regression models and feed-forward neural networks on the academic failure data set.MethodsThe data for this study included 18 questions about study situation of 275 undergraduate students selected randomly from among nursing and midwifery and paramedic schools of Hormozgan University of Medical Sciences in 2013. Logistic regression with forward method and feed forward Artificial Neural Network with 15 neurons in hidden layer were fitted to the dataset. The accuracy of the models in predicting academic failure was compared by using ROC (Receiver Operating Characteristic) and classification accuracy.ResultsAmong nine ANNs, the ANN with 15 neurons in hidden layer was a better ANN compared with LR. The Area Under Receiver Operating Characteristics (AUROC) of the LR model and ANN with 15 neurons in hidden layers, were estimated as 0.55 and 0.89, respectively and ANN was significantly greater than the LR. The LR and ANN models respectively classified 77.5% and 84.3% of the students correctly.ConclusionBased on this dataset, it seems the classification of the students in two groups with and without academic failure by using ANN with 15 neurons in the hidden layer is better than the LR model.

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