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Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm.

Xing Qu1, Chao Zhang2, Shannon H Houser3

  • 1Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu 610041, China.

Computer Methods and Programs in Biomedicine
|November 17, 2022
PubMed
Summary
This summary is machine-generated.

A new caries risk prediction model (CRPM) accurately identifies children under six at risk for new cavities using simple nonbiological questions. This tool aids public health initiatives for early dental care intervention.

Keywords:
Caries risk prediction modelDental-related behaviorsEarly childhood cariesMachine learning algorithm

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Area of Science:

  • Pediatric Dentistry
  • Public Health
  • Machine Learning in Healthcare

Background:

  • Accessible caries risk prediction models (CRPMs) using nonbiological factors are needed for community screening.
  • Developing such a CRPM is crucial for children's dental health promotion via public health strategies.

Purpose of the Study:

  • To develop and validate a machine learning-based CRPM for children.
  • The model utilizes dental care behaviors and nonbiological factors.
  • A 3-month multicenter cohort study design was employed.

Main Methods:

  • Recruited children aged 12-60 months from primary care and kindergarten settings.
  • Conducted dental examinations and collected parental questionnaire data on dental-related factors.
  • Applied machine learning algorithms (random forest, logistic regression, adaptive boosting) for model development and internal validation.

Main Results:

  • Analyzed 481 children; 13.6% developed new caries over 3 months.
  • Key predictors included age, family history, brushing habits, fluoride use, and feeding practices.
  • Random Forest model achieved the highest AUC of 0.91, demonstrating strong predictive performance.

Conclusions:

  • A 12-question CRPM effectively predicts new-onset dental caries in children under 60 months within 3 months.
  • The model shows good performance in internal validation.
  • Results can guide dental home care improvements before caries onset.