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Particle Swarm Optimization over back propagation neural network for length of stay prediction

Publication Type : Conference Paper

Publisher : Procedia Computer Science ICICT 2014

Source : Procedia Computer Science ICICT 2014, Elsevier (2015)

Url :

Keywords : Back propagation neural networks, Backpropagation, Backpropagation algorithms, Error convergence, Forecasting, Health care, Health-care system, length of stay, Neural networks, Optimization, Particle swarm optimization (PSO), Prediction model, Stochastic optimization techniques

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2015

Abstract : Length of stay of an inpatient reflects the severity of illness as well as the practice patterns of the hospital. Predicting the length of stay will provide a better perception of the different resources consumed in a healthcare system. Neural network trained using back propagation has been discerned as a successful prediction model in healthcare systems 1. In this paper, a robust stochastic optimization technique called Particle Swarm Optimization (PSO) is compared with back propagation for training. The algorithms were evaluated based on error convergence, sensitivity, specificity, positive precision value and accuracy and corresponding results are presented. © 2015 The Authors.

Cite this Research Publication : A. Suresh, Harish, K. V., Dr. Radhika N., Samuel P., James R.K., Raj S., and B., P., “Particle Swarm Optimization over back propagation neural network for length of stay prediction”, in Procedia Computer Science ICICT 2014, 2015.

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