Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes

Abstract Introduction Type 2 diabetes mellitus (T2DM) poses a major public health challenge, particularly in regions with limited representation in predictive modeling studies. This research aimed to develop and interpret robust machine learning (ML) models for early T2DM risk prediction using data...

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Autors principals:	Zahra Rafie, Moslem Sedaghat Talab, Behrooz Ebrahim Zadeh Koor, Ali Garavand, Cirruse Salehnasab, Mustafa Ghaderzadeh
Format:	Artigo
Idioma:	Inglês
Publicat:	BMC 2025-10-01
Col·lecció:	BMC Public Health
Matèries:	Type 2 diabetes Machine learning XGBoost CatBoost Explainable AI SHAP
Accés en línia:	https://doi.org/10.1186/s12889-025-24953-w
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