Optimizing Dental Care Resources Using Predictive Analytics: A Data-Driven Approach to Affordable Healthcare

The rising cost of dental care and inefficient utilization of clinical resources pose significant challenges to achieving affordable and equitable oral healthcare. This study proposes a data-driven predictive analytics framework to optimize dental care resource allocation and improve operational efficiency. Using historical dental healthcare data, multiple machine learning models were implemented to forecast appointment reliability and service demand. The predictive outputs were integrated into a resource optimization strategy focusing on workforce deployment, chair utilization, and appointment scheduling. Experimental results demonstrate that ensemble-based models, particularly Random Forest, achieved superior predictive performance. The optimized framework resulted in increased resource utilization, reduced patient waiting times, lower appointment no-show rates, and substantial reductions in operational costs. The findings highlight the effectiveness of predictive analytics in supporting evidence-based decision-making and improving affordability in dental healthcare delivery, offering a scalable approach for both public and private dental care systems