Modeling of Medical Waste Generation in Dental Clinics Affiliated to the Provincial Health Directorate in Kastamonu: PLS and Gradient Boosting Approaches

Effective medical waste planning relies on the reliable estimation of waste volumes. As operational factors diversify, traditional linear regressions often fail to capture the underlying structure, whereas latent variable–based and ensemble approaches can better represent this complexity. In this study, fine-tuned Partial Least Squares (PLS), scikit-learn–based Gradient Boosting regression (GBR), and a baseline Ordinary Least Squares (OLS) model were compared for estimating medical waste generation using 48 months (2021–2024) of approximate data from Dental Clinics affiliated with the Provincial Health Directorate in Kastamonu. The model inputs were the monthly procedure counts for endodontics, treatment, prosthetics, periodontology, orthodontics, pedodontics, and surgery. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared (R2). All models produced accurate predictions; however, PLS provided the strongest fit (R2 = 0.979; MAE = 30.488; RMSE = 37.043), outperforming GBR (R2 = 0.962; MAE = 36.544; RMSE = 48.990) and the OLS baseline (R2 = 0.927; MAE = 41.762; RMSE = 59.013). The findings demonstrate that modern, data-driven waste-management planning is feasible in healthcare institutions and highlight PLS as a robust option, particularly under conditions of small sample size and collinearity.