Machine Learning-Based Predictive Analytics for Dental Practice Management

Abstract

In the rapidly evolving landscape of healthcare, the integration of machine learning (ML) and predictive analytics has emerged as a powerful tool for enhancing operational efficiency and improving patient outcomes. This research paper focuses on the application of ML-based predictive analytics in dental practice management, aiming to develop models that optimize various aspects of dental clinics. The study explores the use of historical data to predict patient attendance, optimize appointment scheduling, manage inventory, and improve revenue forecasting. By leveraging ML algorithms, such as decision trees, random forests, and neural networks, this research aims to provide actionable insights that can help dental clinics streamline their operations and enhance the overall patient experience. The findings of this research have the potential to revolutionize dental practice management, leading to improved efficiency, cost savings, and better patient care.