DOI: 10.66235/kumj.1894369 ISSN: 2757-9336

Explainable ensemble machine learning models for revenue forecasting in multidisciplinary dental clinics: The role of orthodontic production variables

Ergin Kalkan, İbrahim Budak
Show PDF Cite
Aims: Accurate revenue forecasting is essential for effective financial planning in dental healthcare services; however, increasing variability in multidisciplinary clinical workload limits the adequacy of traditional linear approaches. This study aimed to develop and compare explainable machine learning–based models for forecasting monthly revenue in a multidisciplinary public dental clinic and to evaluate the specific contribution of orthodontic production within this framework.Methods: Monthly aggregate data covering 48 months (2021–2024) from dental clinics affiliated with the Kastamonu Provincial Health Directorate were analyzed. Independent variables consisted of monthly procedure volumes in endodontics, restorative treatment, prosthodontics, periodontology, orthodontics, pedodontics, and oral surgery, while total monthly sales constituted the dependent variable. Six regression models were developed using the scikit-learn library: ExtraTreesRegressor, HistGradientBoostingRegressor, ElasticNet, KNeighborsRegressor, MLPRegressor, and a Stacking Regressor combining ExtraTrees and HistGradientBoosting with a Ridge meta-learner. A preprocessing pipeline including RobustScaler and log1p transformation was applied where appropriate. Model performance was evaluated using R², mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Model interpretability was assessed using SHapley Additive exPlanations (SHAP).Results: All models achieved acceptable predictive performance; however, the Stacking model provided the best overall fit (R² ≈ 0.95) and the lowest MAE, RMSE, and MAPE (≈18%). Tree-based ensemble methods consistently outperformed distance-based and neural network approaches. ElasticNet demonstrated competitive performance (R² ≈ 0.91), indicating a strong linear component in revenue dynamics. SHAP analysis revealed that prosthodontic and restorative procedure volumes were the dominant drivers of monthly revenue, whereas orthodontic production had a comparatively modest short-term contribution.Conclusion: Ensemble-based machine learning models, particularly stacking approaches, can accurately forecast monthly revenue in multidisciplinary dental clinics using clinical production indicators. Prosthodontic and restorative services represent the primary drivers of short-term revenue fluctuations, while orthodontic activity contributes more modestly within a monthly time frame. These findings support the integration of explainable machine learning models into financial planning and operational decision-making processes in dental healthcare institutions.

More from our Archive