Integrating Boruta, LASSO, and SHAP for Clinically Interpretable Glioma Classification Using Machine Learning

<b>Background:</b> Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-driven approach for glioma classification by...

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Autors principals:	Mohammad Najeh Samara, Kimberly D. Harry
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-06-01
Col·lecció:	BioMedInformatics
Matèries:	glioma classification machine learning feature selection Boruta Least Absolute Shrinkage and Selection Operator (LASSO) SHapley Additive exPlanations (SHAP)
Accés en línia:	https://www.mdpi.com/2673-7426/5/3/34
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