AcolheEdu: Predicting Psychosocial Vulnerability in the School Context with Histogram-Based Gradient Boosting Trees - Brum, Beatriz; Dembinski, Iasmin; Vasconcellos, Cristhiano; Santos, Carlos (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.20169, 2026)
AcolheEdu proposes a data-driven screening approach to support schools in the early identification of students potentially vulnerable to psychological distress. As a proof of concept, it uses public, anonymized PeNSE 2019 microdata and a calibrated Machine Learning model to estimate a risk score from self-reported psychosocial factors. The calibrated HistGradientBoosting achieved ROC-AUC 0.86 and PR-AUC 0.77 in stratified cross-validation, and ROC-AUC 0.859 on a holdout test set. With an operational threshold of 0.30, recall reached 80.1% with 61.7% precision. The product flow is currently a navigable Figma prototype; REST API integration (FastAPI) is planned for implementation. These results indicate feasibility for high-sensitivity, data-driven school mental health screening.