Predictive Modeling of Dental Health Outcomes Using Machine Learning

Abstract

This study aims to develop predictive models using machine learning techniques to forecast dental health outcomes. Dental health is a crucial aspect of overall well-being, and predicting outcomes can significantly improve treatment planning and patient care. Machine learning, with its ability to analyze complex data patterns, offers a promising approach for this task. This paper explores the development and evaluation of machine learning models for predicting dental health outcomes based on various input features, such as patient demographics, dental history, and treatment plans. The models are trained and tested on a dataset of dental patient records, and their performance is assessed using metrics like accuracy, sensitivity, and specificity. The results demonstrate the potential of machine learning in predicting dental health outcomes and its implications for personalized treatment strategies.