Artificial Neural Network to Modeling Zero-inflated Count Data: Application to Predicting Number of Return to Blood Donation

Shima Haghani, Morteza Sedehi, Soleiman Kheiri


Background: Traditional statistical models often are based on certain presuppositions and limitations that may not presence in actual data and lead to turbulence in estimation or prediction. In these situations, artificial neural networks (ANNs) could be suitable alternative rather than classical statistical methods.

Study design:  A prospective cohort study.

Methods: The study was conducted in Shahrekord Blood Transfusion Center, Shahrekord, central Iran, on blood donors from 2008-2009. The accuracy of the proposed model to prediction of number of return to blood donations was compared with classical statistical models. A number of 864 donors who had a first-time successful donation were followed for five years. Number of return for blood donation was considered as response variable. Poisson regression (PR), negative binomial regression (NBR), zero-inflated Poisson regression (ZIPR) and zero-inflated negative binomial regression (ZINBR) as well as ANN model were fitted to data. MSE criterion was used to compare models. To fitting the models, STATISTICA 10 and, R 3.2.2 was used

Results: The MSE of PR, NBR, ZIPR, ZINBR and ANN models was obtained 2.71, 1.01, 1.54, 0.094 and 0.056 for the training and 4.05, 9.89, 3.99, 2.53 and 0.27 for the test data, respectively.

Conclusions: The ANN model had the least MSE in both training, and test data set and has a better performance than classic models. ANN could be a suitable alternative for modeling such data because of fewer restrictions.


Blood donation; Count regression Models; Zero-inflated count data; Artificial neural network

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