Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches

Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificia...

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Veröffentlicht in:	Frontiers in endocrinology (Lausanne) Jg. 15; S. 1444282
Hauptverfasser:	Arslan, Ahmet Kadir, Yagin, Fatma Hilal, Algarni, Abdulmohsen, Karaaslan, Erol, Al-Hashem, Fahaid, Ardigò, Luca Paolo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Media S.A 11.11.2024
Schlagworte:	Adult Aged Algorithms Artificial Intelligence biomarkers Biomarkers - metabolism Case-Control Studies Diabetes Mellitus, Type 2 - metabolism Endocrinology explainable artificial intelligence Female Humans Machine Learning Male metabolomics Metabolomics - methods Middle Aged predictive modeling Prognosis type 2 diabetes explainable artificial intelligence type 2 diabetes machine learning metabolomics biomarkers predictive modeling
ISSN:	1664-2392, 1664-2392
Online-Zugang:	Volltext
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