Estimating Medical Waste Generation Utilizing Penalized Regression Models

Authors : Burcu Devrim Içtenbaş

Pages : 13-18

View : 34 | Download : 21

Publication Date : 2023-07-31

Article Type : Research Article

Abstract :Medical Waste (MW) amount that has a significant impact on health and environment is increasing as a result of industrialization as well as population density. There is a need an accurate estimation waste generation amount that will be useful information to select the appropriate disposal methods and to organize the recycling and storage. Some researchers have applied conventional statistical algorithms and many kinds of Machine Learning (ML) algorithms to predict MW amount. However, to the best of our knowledge, penalized regression methods such as Ridge, Lasso, and Elastic Net regressions have not been used to predict the MW amount. 18-years real data were obtained from İstanbul Metropolitan Municipality Department Open Data Portal with the input variables namely number of hospitals, number of health personal, number of bed available at the hospital, crude birth rate and gross domestic product per capita. 80% of the total database being used for developing the models, whereas the rest 20% were used to validate the models. In order to compare their performances, 5-fold cross-validation was applied and performance measures (MAE, RMSE and R-squared) were calculated in this study. Of the penalized regression methods, the Lasso regression provided better performance than those of other models with RMSE, MAE, and R-squared of 349.56, 596.52, 0.96, respectively, whereas the second-best Ridge regression poorer accuracy with RMSE, MAE, and R-squared 1039.091, 878.25,0.88, respectively. Thus, in our case, Lasso regression can be considered better than the Ridge regression and Elastic Net regression due to the lowest RMSE and MAE values and highest R-squared. The results reveal that the proposed Lasso regression is better than the other penalized regression models to predict the MW amount.
Keywords : tıbbi atık, cezalandırılmış regresyon modelleri, tahmin, sürdürülebilirlik