Golbaz Somayeh, Nabizadeh Ramin, Sajadi Haniye Sadat
1Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
2Health Services Management, National Institute for Health Research, Tehran University of Medical Sciences, Tehran, Iran.
J Environ Health Sci Eng. 2019 Feb 26;17(1):41-51. doi: 10.1007/s40201-018-00324-z. eCollection 2019 Jun.
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.
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 (R) and Mean-Square Error (MSE).
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 R value and 0.003-0.008 for average MSE value, indicated relative superiority of Kernel-based models compared to Neuron based (average R = 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.
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.
成功的医院固体废物（HSW）管理需要准确估计废物产生率。传统回归方法在增加输入变量数量时很难预测HSW产生率，并且需要更复杂的建模。相比之下，应用机器学习方法似乎能够提高预测产生废物的能力。
为了预测HSW，采用多元线性回归（MLR）以及几种基于神经元和核的机器学习方法来分析卡拉季市医院的数据。病房数量、活跃和占用床位、工作人员和住院患者数量，以及医院的所有权类型和运营年限被定义为模型输入。此外，基于决定系数（R）和均方误差（MSE）对所提出模型的性能进行评估。
基于神经元和核的机器学习方法的性能表明，这两种模型在预测HSW方面都令人满意。然而，平均R值为0.82 - 0.86，平均MSE值为0.003 - 0.008的更好结果表明，与基于神经元的模型（平均R = 0.68 - 0.74，平均MSE = 0.009 - 0.023）和MLR模型相比，基于核的模型具有相对优势。工作人员数量和医院所有权类型是预测HSW产生率时最具影响力的模型变量。
机器学习方法能够适当地解释废物产生率与模型输入之间的关系。因此，它们可能在开发具有成本效益的合适HSW管理方法中发挥有效作用。
