Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey

随机森林 Boosting(机器学习) 平均绝对百分比误差 均方误差 阿达布思 集成学习 梯度升压 线性回归 机器学习 计算机科学 回归 人工智能 集合预报 回归分析 统计 绝对偏差 数学 支持向量机
作者
Babak Daneshvar Rouyendegh,Burcu Devrim-İçtenbaş
出处
期刊:Mathematics [Multidisciplinary Digital Publishing Institute]
卷期号:10 (14): 2466-2466 被引量:35
标识
DOI:10.3390/math10142466
摘要

Predicting medical waste (MW) properly is vital for an effective waste management system (WMS), but it is difficult because of inadequate data and various factors that impact MW. This study’s primary objective was to develop an ensemble voting regression algorithm based on machine learning (ML) algorithms such as random forests (RFs), gradient boosting machines (GBMs), and adaptive boosting (AdaBoost) to predict the MW for Istanbul, the largest city in Turkey. This was the first study to use ML algorithms to predict MW, to our knowledge. First, three ML algorithms were developed based on official data. To compare their performances, performance measures such as mean absolute deviation (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were calculated. Among the standalone ML models, RF achieved the best performance. Then, these base models were used to construct the proposed ensemble voting regression (VR) model utilizing weighted averages according to the base models’ performances. The proposed model outperformed three baseline models, with the lowest RMSE (843.70). This study gives an effective tool to practitioners and decision-makers for planning and constructing medical waste management systems by predicting the MW quantity.
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