Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study.

ABSTRACT:

Objective

To develop a machine learning (ML) model utilizing transfer learning (TL) techniques to predict hypertension in children and adolescents across South America.

Methods

Data from two cohorts (children and adolescents) in seven South American cities were analyzed. A TL strategy was implemented by transferring knowledge from a CatBoost model trained on the children's sample and adapting it to the adolescent sample. Model performance was evaluated using standard metrics.

Results

Among children, the prevalence of normal blood pressure was 88.9% (301 participants), while 14.1% (50 participants) had elevated blood pressure (EBP). In the adolescent group, the prevalence of normal blood pressure was 92.5% (284 participants), with 7.5% (23 participants) presenting with

SUBMITTER: Araujo-Moura K 

PROVIDER: S-EPMC11937837 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature