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Estimating The Number Of Daily Patient Applications By Using Artificial Neural Networks

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  • H Social Sciences (General)

Abstract

This study is aiming at estimating the patient volumes of hospitals by using artificial neural networks. In order to train the artificial neural network models in this study, historical patient applications data from a Turkish hospital were used. All patient applications counted as daily numbers during three years and dependent variable of our study (patient_count) is derived. A different approach used in this study and instead of a single independent variable (which is time), four different time periods were used as input variables of the artificial neural network models. These input variables were day of month, day of week, month, and year. Several artificial neural network models have been generated and compared with each other by their predictive performance measures. The best predictive artificial neural network architecture has an estimation accuracy of 94.22 percent. This artificial neural network model has an input layer with four neurons, an output layer with one neuron, and only one hidden layer with nineteen neurons. The arithmetic mean of patient application in a day is 755.93 (S.d.=486.60). Mean error of the artificial neural network model is -0.047 and mean absolute error is 105.64. The linear correlation between the actual values and the predicted values of the number of patients is 0.918. Keywords: artificial neural networks, decision support systems, modeling, estimation, hospital management.

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