Prediction Length of Stay with Neural Network Trained by Particle Swarm Optimization

Abstract

This article describes how the prediction of the length of stay demonstrates the severity of the disease as well as the practice patterns of hospitals. Also, it helps the hospital resources management provide better services for inpatients and increase inpatients' satisfaction. In this article, an efficient model based on neural network algorithms is trained by a stochastic optimization technique called particle swarm optimization is proposed to predict the length of stay for coronary artery diseases. Real world data is used to generate the model. According to the number of missing values, some policies are considered. Since the outlier data has negative impact on the prediction model, it would be eliminated. The parameters of the proposed model are adjusted by Taguchi method. The applied algorithm evaluation result on the test data indicates that the model has the capability to predict the length of stay with 90 percent accuracy.