The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation
- Department of Hospital Management, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of)
- Department of Medical Physics, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of)
- Department of Biophysics, Faculty of Science, Tarbiat Modares University, Tehran (Iran, Islamic Republic of)
- Department of Community Medicine, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of)
- Department of Biochemistry, Division of Genetics, Tabriz University of Medical Sciences, Tabriz (Iran, Islamic Republic of)
- Department of Mathematics, Faculty of Science, Vali-E-Asr University, Rafsanjan (Iran, Islamic Republic of)
Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.
- OSTI ID:
- 21269325
- Journal Information:
- Waste Management, Vol. 29, Issue 11; Other Information: DOI: 10.1016/j.wasman.2009.06.027; PII: S0956-053X(09)00242-6; Copyright (c) 2009 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA); ISSN 0956-053X
- Country of Publication:
- United States
- Language:
- English
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