Predictive Modeling of Dental Health Outcomes Based on Fluoride Concentrations using AI

Oral health is a crucial aspect of general health, and the presence of fluoride in drinking water has been consistently linked to its improvement. This work utilizes deep learning and machine learning approaches to develop prediction models that can estimate oral health consequences based on fluoride concentrations. Our analysis is based on a comprehensive dataset that includes fluoride levels and oral health indicators from several geographic locations. The dataset covers a broad range of demographic and environmental factors. The study involves a thorough data pretreatment procedure, which includes activities such as data cleaning, standardization, and feature engineering. All of these processes contribute to improving and making the input variables more relevant. The approach used encompasses a range of machine learning algorithms, including as neural networks, decision trees, and ensemble approaches, which are used to create prediction models. Thoroughly adjusting hyperparameters and using cross-validation methods are used to maximize the effectiveness of the model.