A Comparative Analysis of Machine Learning Approaches for Predicting Dental Fluorosis Risk from Groundwater Fluoride Contamination
Keywords:
M-517_International Journal of Intelligent Systems and Applications in Engineering Vol. 12 No. 23s (2024) 2024-12-12Dental Fluorosis, Groundwater Fluoride Contamination, Machine Learning, Predictive Models, Multilayer PerceptronAbstract
Dental fluorosis is a significant public health concern caused by excessive intake of groundwater fluoride contamination, particularly in regions with high natural fluoride deposits. This study systematically explores various Machine Learning (ML) approaches used in predicting dental fluorosis risk, focusing on key techniques such as Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost). Following a comprehensive literature search from 2019 to 2024 using databases like Scopus, IEEE, Web of Science, and Springer, this analysis adheres to PRISMA guidelines to ensure methodological rigor. Studies demonstrated promising results, with advanced models like SVM and MLP achieving high prediction accuracy rates of up to 97.4%. The key observation of this study is that these advanced models outperform traditional methods in predicting fluoride contamination risks. However, challenges remain, particularly in developing region-specific models, improving data collection, and addressing computational inefficiencies. This study provides a detailed comparative analysis of these techniques and identifies research gaps, including the need for scalable models and improved real-time prediction for effective public health interventions.
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