2025
Conférence
Second Edition IEEE Afro-Mediterranean Conference on Artificial Intelligence 2025 IEEE AMCAI IEEE AMCAI 2025
Emergency departments (EDs) must quickly assess
and prioritize patients, especially during crises when demand
exceeds capacity. Traditional triage methods, such as the Jump-
START protocol for pediatric cases and the START (Simple
Triage and Rapid Treatment) method for adults, are commonly
used but may lack precision under high-pressure situations.
This paper proposes a hybrid approach combining ensemble
models—XGBoost, AdaBoost, and CatBoost—with synthetic data
augmentation using Generative Adversarial Networks (GANs) to
enhance triage accuracy for critically ill patients.
Models were trained on real-world ED data, including vital
signs, symptoms, medical history, and demographics. GANs
generated synthetic critical cases to address class imbalance,
improving model sensitivity to high-risk profiles.
Results show that GAN-augmented models outperform base-
line models, with CatBoost offering the best balance between
accuracy and computational efficiency. This approach improves
patient prioritization, reduces delays, and supports better clinical
decision-making in resource-limited environments.
Index Terms—Emergency Department (ED), Patient Triage,
Machine Learning (ML), AdaBoost, XGBoost, CatBoost, Genera-
tive Adversarial Networks (GANs), Urgency Classification, Crisis
Situations.
Amel ZIDI 
Ines Ben Jaafar