Machine Learning Approaches for Assessing Fluoride Concentration in Drinking Water and Dental Health

Fluoride in drinking water has been a significant issue in recent years due to speculation about positive benefits on dental health. When fluoride levels are too high, dental fluorosis may develop on the enamel and remain permanently. Traditional methods of determining the concentration of fluoride are often laborious and time consuming, not to mention non-real time. This study introduces an ML method for predicting and assessing the effects of fluoride in drinking water on tooth health. In this study, a wide variety of ML models, such as Decision Trees, Neural Networks, and Support Vector Machines, are employed to analyze a dataset consisting of water samples from various locations and oral health characteristics. This approach may be beneficial for detecting fluoride levels in real-time and identifying their impact on dental health, since our best-performing model had an accuracy of 96.4%. This not only paves the way for proactive water quality management, but it also helps communities anticipate and avoid dental health risks caused by changing fluoride concentrations.