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Predicting dental caries outcomes in young adults using machine learning approach
Chukwuebuka Ogwo1, Grant Brown2, John Warren3
1Department of Oral Health Sciences, Temple University Maurice H Kornberg School of Dentistry, 3223 N Broad Street, L216, Philadelphia, PA, 19131, US. Chukwuebuka.ogwo@temple.edu.
BMC Oral Health
|May 3, 2024
Summary
Machine learning accurately predicts dental caries in young adults using past caries experience and sugar-sweetened beverage intake as key predictors. This approach can help identify at-risk individuals for targeted public health interventions.
Area of Science:
- Oral Health
- Data Science
- Public Health
Background:
- Dental caries remains a significant public health issue, particularly among young adults.
- Predictive modeling offers a promising avenue for early identification and intervention.
- Longitudinal data analysis is crucial for understanding disease progression and risk factors.
Purpose of the Study:
- To predict dental caries outcomes in young adults using longitudinal data.
- To identify key predictors of caries development using machine learning techniques.
- To evaluate the performance of different machine learning models for caries prediction.
Main Methods:
- Utilized the Iowa Fluoride Study dataset with longitudinal predictor variables.
- Compared LASSO regression, GBM, NegGLM, and XGBOOST models via 5-fold cross-validation.
- Included demographic, socioeconomic, fluoride, dietary, and behavioral factors as predictors.
Main Results:
- LASSO regression demonstrated superior performance with an R² of 0.44 and RMSE of 0.70.
- Classification accuracy, precision, and recall reached 83.7%, 85.9%, and 93.1%, respectively.
- Past caries experience (ages 13, 17) and sugar-sweetened beverage intake (ages 13, 17) were the strongest predictors.
Conclusions:
- Machine learning models can accurately predict caries in young adults using longitudinal data.
- Identified key predictors can inform targeted public health screening and interventions.
- Further validation with diverse populations is recommended to enhance generalizability.
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