SIMONE SEIXAS DA CRUZ, Edilson José Rodrigues, Michelle de Santana Xavier Ramos, Débora Conceição Santos de Oliveira, Paulo José dos Santos de Matos, Julita Maria Freitas Coelho, Dóris Firmino Rabelo, Alexandre Marcelo Hintz, Johelle Santana Passos‐Soares, Ana Cláudia Morais Godoy Figueiredo, Isaac Suzart Gomes‐Filho
BACKGROUND/OBJECTIVES: The high prevalence of hearing impairment in older adults and the scarcity of diagnostic resources in primary health care (PHC) highlight the need for solutions based on artificial intelligence. This study aimed to develop and evaluate a decision tree model to prioritize hearing examinations in this population. METHODS: An observational study was conducted using 60 clinical profiles. The decision tree model was developed in R, using the Recursive Partitioning and Regression Trees (rpart) package after data preprocessing that included cleaning, dichotomization, and multiple imputations by chained equations (MICE). The algorithm was trained on 70% of the sample and tested on the remaining 30%. Its performance was evaluated using standard metrics. RESULTS: The model achieved satisfactory performance with good accuracy, sensitivity, and specificity. The area under the curve (AUC) was 0.825, outperforming a classic logistic regression model (AUC 0.700). Based on the model's key predictors, which include the presence of tinnitus, not having a partner, female sex, hypertension and/or diabetes, and age ≥ 65 years, a user-friendly, interactive web-based dashboard was developed for clinical use. CONCLUSION: The decision tree model is a promising tool that demonstrates technical viability for prioritizing hearing loss screening. While further large-scale external validation is required, it serves as a foundational step for integrating AI-driven prioritization in primary health care.