Dental services use prediction among adults in Southern Brazil: A gender and racial fairness-oriented machine learning approach

作者：Luiz Alexandre Chisini, Cinthia Fonseca Araujo, Felipe Mendes Delpino, Lílian Munhoz Figueiredo, Alexandre Dias Porto Chiavegatto Filho, Helena Silveira Schuch, Bruno Pereira Nunes, Flávio Fernando Demarco · 发表于：Journal of Dentistry · 年份：2025 · DOI：10.1016/j.jdent.2025.105929 · 被引用次数：12 · 研究领域：Dental Health and Care Utilization、Oral microbiology and periodontitis research、Dental Anxiety and Anesthesia Techniques

OBJECTIVE: To develop machine learning models to predict the use of dental services among adults aged 18 and older. METHODS: This is a prospective cohort study that uses data from the survey "EAI Pelotas?". The sample consisted of individuals who participated in both the baseline and follow-up, totaling 3461 people. Predictors were collected as baseline and comprised 47 sociodemographic, behavioral, oral and general health characteristics. The outcome was dental service use in the last year assessed during the one-year follow-up. Data was divided into training (80 %) and test (20 %) sets. Five machine learning models were tested. Hyperparameter tuning was optimized through 10-fold cross-validation, utilizing 30 iterations. Model performance was assessed based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), accuracy, recall, precision, and F1-score. RESULTS: The prevalence of dental service use in the follow-up was 47.2 % (95 % CI, 45.5 - 48.9). All models in the test set demonstrated an AUC-ROC between 0.76 and 0.77. The CatBoost Classifier model exhibited the highest performance in the test dataset among the models concerning the AUC metric (AUC = 0.77, CI95 %,[0.73-0.80]), displaying an accuracy = 0.69, recall = 0.69, precision = 0.68, and F1-score = 0.69. Fairness estimations for the best model indicated consistent performance across gender categories. However, disparities were observed among racial groups, AUC = 0.57 for individuals who self-rep...