The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation.
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(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(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.
Jahandideh
Sepideh
S
Department of Hospital Management, Shiraz University of Medical Sciences, Shiraz, Iran.
Jahandideh
Samad
S
Asadabadi
Ebrahim Barzegari
EB
Askarian
Mehrdad
M
Movahedi
Mohammad Mehdi
MM
Hosseini
Somayyeh
S
Jahandideh
Mina
M
D005544:Forecasting; D006761:Hospitals / Q000639:trends; D016014:Linear Models; D008506:Medical Waste / Q000032:analysis / Q000706:statistics & numerical data*; D016571:Neural Networks, Computer; D018505:Waste Management / Q000379:methods
D008506:Medical Waste