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Soft Computing Model to Predict Average Length of Stay of Patient

  • Bindu Garg
  • M. M. Sufyan Beg
  • A. Q. Ansari
  • B. M. Imran
Part of the Communications in Computer and Information Science book series (CCIS, volume 141)

Abstract

Forecasting the average Length of Stay (LoS) of a patient is prime aspect for all hospitals to effectively determine and plan services demanded at various level. Prediction of LoS plays a vital role in strategic decision making by health care administrators. In this paper, a dynamic computational model based on time series, implemented using soft computing techniques is presented to forecast average length of stay of patient. Aim of designing proposed model is to overcome the drawbacks of the exiting approaches and derive more robust and accurate methodology to forecast LoS of patient. Subsequently, the performance of the proposed model is demonstrated by comparing the results of proposed model with some of the pre-existing forecasting methods. In general, the findings of the study are interesting and superior in terms of least Average Forecasting Error Rate (AFER) and Mean Square Error (MSE) values.

Keywords

Time series soft computing fuzzy logic average length of stay (LoS) average forecasting error rate mean square error 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bindu Garg
    • 1
  • M. M. Sufyan Beg
    • 1
  • A. Q. Ansari
    • 2
  • B. M. Imran
    • 1
  1. 1.Department of Computer EngineeringJamia Millia IslamiaNew DelhiIndia
  2. 2.Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia

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