A Proposed Recommender System for Dental Care Treatments Based on Ensemble Learning

This study utilizes the ۲۰۱۵-۲۰۱۶ National Health and Nutrition Examination Survey (NHANES) dataset to construct a comprehensive and unified dataset, that integrates data from multiple sections. Following essential preprocessing steps, we implement a range of conventional machine learning algorithms alongside ensemble methods to develop a recommendation system for oral and dental health care. This system assesses patient health conditions to suggest the necessity of dental visits. By analyzing patterns and trends within the dataset, our system can provide personalized recommendations, potentially improving oral health outcomes. Our results underscore the superior performance of ensemble models, particularly the stacking method, demonstrating their effectiveness and reliability over traditional machine learning models.

کلیدواژه ها:

Ensemble Learning ، Stacking Model ، Dental Care Recommendations ، NHANES Dataset ، Machine Learning in Dentistry