Somayeh Golbaz1, Ramin Nabizadeh1, Haniye Sadat Sajadi2. 1. 1Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. 2. 2Health Services Management, National Institute for Health Research, Tehran University of Medical Sciences, Tehran, Iran.
Abstract
PURPOSE: A successful hospital solid waste (HSW) management needs an accurate estimation of waste generation rates. The conventional regression methods upon increasing the number of input variables hardly can predict the HSW generation rate and require more complex modeling. In return, application of machine learning methods seems to be able to increase the power of predicting the produced wastes. METHODS: To predict the HSW, Multiple Linear Regression(MLR) and several Neuron- and Kernel-based machine learning methods were employed to analyze data from hospitals of Karaj metropolis. The number of wards, active and occupied beds, staffs and inpatients, and ownership type and activity years of hospital were defined as the model inputs. In addition, proposed models performance was evaluated based on coefficient of determination (R2) and Mean-Square Error (MSE). RESULTS: The performance of Neuron- and Kernel-based machine learning methods indicated that both models were satisfactory in predicting HSW. However, the better results of 0.82-0.86 for average R2 value and 0.003-0.008 for average MSE value, indicated relative superiority of Kernel-based models compared to Neuron based (average R2 = 0.68-0.74, average MSE = 0.009-0.023) and MLR models. Number of staffs and hospital ownership type were the most influential model variables in predicting the HSW generation rate. CONCLUSIONS: The machine learning methods could interpret the relationship between waste generation rate and model inputs, appropriately. Thus, they may play an effective role in developing cost-effective methods for suitable HSW management.
PURPOSE: A successful hospital solid waste (HSW) management needs an accurate estimation of waste generation rates. The conventional regression methods upon increasing the number of input variables hardly can predict the HSW generation rate and require more complex modeling. In return, application of machine learning methods seems to be able to increase the power of predicting the produced wastes. METHODS: To predict the HSW, Multiple Linear Regression(MLR) and several Neuron- and Kernel-based machine learning methods were employed to analyze data from hospitals of Karaj metropolis. The number of wards, active and occupied beds, staffs and inpatients, and ownership type and activity years of hospital were defined as the model inputs. In addition, proposed models performance was evaluated based on coefficient of determination (R2) and Mean-Square Error (MSE). RESULTS: The performance of Neuron- and Kernel-based machine learning methods indicated that both models were satisfactory in predicting HSW. However, the better results of 0.82-0.86 for average R2 value and 0.003-0.008 for average MSE value, indicated relative superiority of Kernel-based models compared to Neuron based (average R2 = 0.68-0.74, average MSE = 0.009-0.023) and MLR models. Number of staffs and hospital ownership type were the most influential model variables in predicting the HSW generation rate. CONCLUSIONS: The machine learning methods could interpret the relationship between waste generation rate and model inputs, appropriately. Thus, they may play an effective role in developing cost-effective methods for suitable HSW management.
Entities:
Keywords:
Hospital; Machine learning method; Multiple linear regression; Solid waste
Authors: Gi-Wook Cha; Hyeun Jun Moon; Young-Min Kim; Won-Hwa Hong; Jung-Ha Hwang; Won-Jun Park; Young-Chan Kim Journal: Int J Environ Res Public Health Date: 2020-09-24 Impact factor: 3.390
Authors: Arthur Couto Neves; Camila Costa Maia; Maria Esther de Castro E Silva; Gisele Vidal Vimieiro; Marcos Paulo Gomes Mol Journal: Environ Sci Pollut Res Int Date: 2022-07-23 Impact factor: 5.190