Comparison of Artificial neural network model with Count data Regression models for Prediction of blood Donation

Modeling is one of the most important ways for explanation of relationship between dependent and independent response. Since data, related to number of blood donations are discrete, to explain them it is better to use discrete variable distribution like Poison or Negative binomial. This research tries to analyze numerical methods by using neural network approach and compare it by classic statistical methods to choose better way to predict the number of blood donations. In this study, data were collected from blood donors at the blood center of the Sharekord and then four methods were compared by neural network approach. These methods are: Poisson regression model and its zero inflated, Negative binomial models and its zero inflated.To learn neural network approach, (BFGS) Broyden–Fletcher–Goldfarb–Shanno algorithm was used. To choose the best model, mean-square error (MSE) was used. The best network structure in teaching data was chosen and neural network approach resolution was compared by them, to choose the best approach for prediction the number of blood donations. The MSE for Poisson regression model, Poisson regression with zero inflated, negative binomial and negative binomial with zero inflated are respectively 2.71, 1.54, 0.94 and 1.01. For neural network approach 14:17:1 with activation function of hyperbolic tangent in hidden layer and output layer 0.056 is achieved. The results showed that, according to amount of MSE, neural network approach is the best method with highest accuracy to predict the number of blood donations rather than other methods examined in this article.