• Predicting negative self-rated...

*Result*: Predicting negative self-rated oral health in adults using machine learning: A longitudinal study in Southern Brazil.

Title:
Predicting negative self-rated oral health in adults using machine learning: A longitudinal study in Southern Brazil.
Authors:
Araujo CF; Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil., Delpino FM; Postgraduate Program in Dentistry, Federal University of Pelotas, Pelotas, Brazil., Figueiredo LM; Postgraduate Program of Nursing, Federal University of Pelotas, Pelotas, Brazil., Chiavegatto Filho ADP; School of Public Health, University of São Paulo, São Paulo, Brazil., Nunes BP; Department of Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL USA., Schuch HS; Postgraduate Program in Dentistry, Federal University of Pelotas, Pelotas, Brazil; School of Dentistry, Faculty of Health, Medical and Behavioural Sciences, The University of Queensland, Brisbane, Australia., Demarco FF; Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil; Postgraduate Program in Dentistry, Federal University of Pelotas, Pelotas, Brazil; Radboud University Medical Center, Department of Dentistry, Nijmegen, the Netherlands. Electronic address: flavio.demarco@radboudumc.nl.
Source:
Journal of dentistry [J Dent] 2025 Dec; Vol. 163, pp. 106164. Date of Electronic Publication: 2025 Oct 09.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: England NLM ID: 0354422 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-176X (Electronic) Linking ISSN: 03005712 NLM ISO Abbreviation: J Dent Subsets: MEDLINE
Imprint Name(s):
Publication: Kidlington : Elsevier
Original Publication: Bristol, Eng., Wright.
MeSH Terms:
Oral Health*/statistics & numerical data , Machine Learning* , Self Report*, Humans ; Brazil ; Longitudinal Studies ; Male ; Adult ; Female ; Middle Aged ; Young Adult ; Aged ; Adolescent ; Algorithms
Contributed Indexing:
Keywords: Artificial intelligence; Dentistry; Machine learning; Oral health
Entry Date(s):
Date Created: 20251011 Date Completed: 20251122 Latest Revision: 20251124
Update Code:
20260130
DOI:
10.1016/j.jdent.2025.106164
PMID:
41075925
Database:
MEDLINE

*Further Information*

*Objective: This study aims to develop and evaluate the performance of machine learning models to predict the occurrence of negative self-rated oral health (SROH) among adults.
Methods: Data were collected through a longitudinal population-based survey conducted in Pelotas, Southern Brazil. The analysis included 3,461 participants with complete data at both baseline and follow-up. Predictors were collected at baseline and encompassed 46 sociodemographic, behavioral, general, and oral health characteristics. The outcome of interest was negative SROH. Data analysis was conducted using Python. The database was divided into training (70%) and testing (30%). The performance of five machine learning algorithms - Random Forest, LightGBM, CatBoost, XGBoost, and TabPFN - was evaluated according to the area under the ROC curve. Additional performance metrics included accuracy, precision, recall, and F1-score. The contribution of each predictor was assessed using Shapley values.
Results: Negative self-rated oral health was reported by 571 individuals (16.6%). The models achieved a performance between 0.671 to 0.715 according to the AUC-ROC, with TabPFN demonstrating the best performance. The most important predictors according to Shapley values were ABEP index scores (socioeconomic indicator), type of dental service used, age, General Anxiety Disorder (GAD-7) scores, and overall life satisfaction.
Conclusions: The machine learning models developed in this study demonstrated a reasonable performance in identifying individuals with negative self-rated oral health. However, they require further refinement to ensure practical applicability in real-world settings, considering their current limitations.
Clinical Significance: Our findings highlighted the potential of using machine learning to predict subjective oral health conditions. This model should be improved to make it feasible for real-world implementation, considering its limitations. The correct identification of individuals with negative self-rated oral health may support the development of targeted strategies focused on these at-risk groups.
(Copyright © 2025. Published by Elsevier Ltd.)*

*Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*