Soft Computing Model to Predict Average Length of Stay of Patient
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.
KeywordsTime series soft computing fuzzy logic average length of stay (LoS) average forecasting error rate mean square error
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