Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals

Authors:
Esmaeil Hadavandi;Hassan Shavandi;Arash Ghanbari;Salman Abbasian-Naghneh
Affiliations:
Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 15875-4413 Tehran, Iran;Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11365-9466, Tehran, Iran;Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran;Department of Mathematics, Islamic Azad University Najafabad Branch, Najafabad, Iran
Venue:
Applied Soft Computing
Year:
2012

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Abstract

Accurate forecasting of outpatient visits aids in decision-making and planning for the future and is the foundation for greater and better utilization of resources and increased levels of outpatient care. It provides the ability to better manage the ways in which outpatient's needs and aspirations are planned and delivered. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani type fuzzy rule based system to forecast outpatient visits with high accuracy. The hybrid model uses genetic algorithm for evolving knowledge base of fuzzy system. Actually it extracts useful patterns of information with a descriptive rule induction approach based on Genetic Fuzzy Systems (GFS). This is the first study on using a GFS to constructing an expert system for outpatient visits forecasting problems. Evaluation of the proposed approach will be carried out by applying it for forecasting outpatient visits of the department of internal medicine in a hospital in Taiwan and four big hospitals in Iran. Results show that the proposed approach has high accuracy in comparison with other related studies in the literature, so it can be considered as a suitable tool for outpatient visits forecasting problems.