An Effective Machine Learning for Prioritizing Hearing Loss Screening in Older Adults: A Primary Care Approach
6 affiliations
- 1Federal University of Recôncavo da Bahia (UFRB) Santo Antônio de Jesus Bahia Brazil
- 2Federal University of ABC São Bernardo Do Campo São Paulo Brazil
- 3State University of Feira de Santana (UEFS) Feira de Santana Bahia Brazil
- 4Federal Institute of Education, Science and Technology of Bahia (IFBA) Salvador Bahia Brazil
- 5Federal University of Bahia (UFBA) Salvador Bahia Brazil
- 6Federal District Health State Department Epidemiology Surveillance Brasília Brazil
Abstract
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.
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BibTeX
@article{cruz2026effective,
title = {{An Effective Machine Learning for Prioritizing Hearing Loss Screening in Older Adults: A Primary Care Approach}},
author = {Simone Seixas da Cruz and Edilson José Rodrigues and Michelle de Santana Xavier Ramos and Débora Conceição Santos de Oliveira and Paulo José dos Santos de Matos and Julita Maria Freitas Coelho and Dóris Firmino Rabelo and Alexandre Marcelo Hintz and Johelle Santana Passos‐Soares and Ana Claudia Morais Godoy Figueiredo and Isaac Suzart Gomes‐Filho},
journal = {Journal of the American Geriatrics Society},
year = {2026},
month = jul,
doi = {10.1111/jgs.70634},
url = {https://doi.org/10.1111/jgs.70634},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An Effective Machine Learning for Prioritizing Hearing Loss Screening in Older Adults: A Primary Care Approach
AU - Simone Seixas da Cruz
AU - Edilson José Rodrigues
AU - Michelle de Santana Xavier Ramos
AU - Débora Conceição Santos de Oliveira
AU - Paulo José dos Santos de Matos
AU - Julita Maria Freitas Coelho
AU - Dóris Firmino Rabelo
AU - Alexandre Marcelo Hintz
AU - Johelle Santana Passos‐Soares
AU - Ana Claudia Morais Godoy Figueiredo
AU - Isaac Suzart Gomes‐Filho
PY - 2026
DA - 2026/07/31
JO - Journal of the American Geriatrics Society
DO - 10.1111/jgs.70634
UR - https://doi.org/10.1111/jgs.70634
AB - 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.
SN - 0002-8614
SN - 1532-5415
ER -