Estimating Medical Waste Generation Utilizing Penalized Regression Models

Year 2023, Volume: 03 Issue: 01, 13 - 18, 31.07.2023

Burcu Devrim İçtenbaş

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

medical waste, penalized regression models, prediction, sustainability

Cezalandırılmış Regresyon Modelleri Kullanılarak Tıbbi Atık Üretiminin Tahmini

Abstract

Sağlık ve çevre üzerinde önemli bir etkiye sahip olan Tıbbi Atık (TAT) miktarı, nüfus yoğunluğunun yanı sıra sanayileşmenin bir sonucu olarak artmaktadır. Uygun bertaraf yöntemlerinin seçilmesi, geri dönüşüm ve depolamanın düzenlenmesi için yararlı bilgiler sağlayacak doğru bir atık üretim miktarı tahminine ihtiyaç vardır. Bazı araştırmacılar MW miktarını tahmin etmek için geleneksel istatistiksel algoritmaları ve birçok Makine Öğrenimi (ML) algoritmasını uygulamıştır. Ancak, bildiğimiz kadarıyla, Ridge, Lasso ve Elastic Net regresyonları gibi cezalandırılmış regresyon yöntemleri MW miktarını tahmin etmek için kullanılmamıştır. 18 yıllık gerçek veriler, İstanbul Büyükşehir Belediyesi Başkanlığı Açık Veri Portalı'ndan hastane sayısı, sağlık personeli sayısı, hastanedeki yatak sayısı, kaba doğum oranı ve kişi başına düşen gayri safi yurtiçi hasıla girdi değişkenleri ile elde edilmiştir. Toplam veri tabanının %80'i modellerin geliştirilmesi için kullanılırken, geri kalan %20'si modellerin doğrulanması için kullanılmıştır. Performanslarını karşılaştırmak için bu çalışmada 5 kat çapraz doğrulama uygulanmış ve performans ölçütleri (MAE, RMSE ve R-kare) hesaplanmıştır. Cezalandırılmış regresyon yöntemlerinden Lasso regresyonu sırasıyla 349.56, 596.52, 0.96 RMSE, MAE ve R-kare ile diğer modellerden daha iyi performans sağlarken, ikinci en iyi Ridge regresyonu sırasıyla 1039.091, 878.25, 0.88 RMSE, MAE ve R-kare ile daha düşük doğruluk sağlamıştır. Dolayısıyla, bizim durumumuzda, Kement regresyonu, en düşük RMSE ve MAE değerleri ve en yüksek R-kare nedeniyle Ridge regresyonu ve Elastik Ağ regresyonundan daha iyi kabul edilebilir. Sonuçlar, önerilen Lasso regresyonunun MW miktarını tahmin etmek için diğer cezalandırılmış regresyon modellerinden daha iyi olduğunu ortaya koymaktadır.

Keywords

tıbbi atık, cezalandırılmış regresyon modelleri, tahmin, sürdürülebilirlik

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