A Data–Driven Approach to the Role Classification of Public Hospitals: Evidence from Türkiye

Authors : Tevfik Bulut, Aziz Küçük

Pages : 803-829

Doi:10.52836/sayistay.1751816

View : 64 | Download : 163

Publication Date : 2025-12-24

Article Type : Research Paper

Abstract :Hospital classification plays a crucial role in modern healthcare systems by promoting efficient resource utilization, enhancing access to care, and directing patients to the most appropriate service levels. This study aimed to predict the role classifications of public hospitals in Türkiye using the Random Forest (RF) classification algorithm. It represents the first study conducted specifically on the role-based classification of public hospitals in Türkiye. The dataset included 716 public hospitals, categorized into eight distinct role classes. Two RF models (Model 1 and Model 2) were developed and evaluated using performance metrics, including overall accuracy, Cohen’s Kappa coefficient, area under the curve (AUC), F1 score, and balanced accuracy. RF Model 2 consistently outperformed Model 1, achieving higher accuracy (96.82% vs. 95.82%), Kappa (0.9612 vs. 0.9489), and AUC (0.9889 vs. 0.9863). Thus, Model 2 is recommended for classifying the roles of public hospitals in Türkiye. The proposed approach can be adapted to classify hospitals in different healthcare systems, offering support for data-driven strategic planning and more equitable resource allocation.
Keywords : Kamu Hastanesi, Rastgele Orman, Makine Öğrenmesi, Hastane Sınıflandırması, Değişken Önem Düzeyi

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