An explainable machine learning approach to predict fragility fractures and the identification of important features

Abstract In this study, we developed ML algorithms to predict fragility fractures, considering the occurrence of fractures at different skeletal sites, using the data from the Canadian Multicentre Osteoporosis Study (CaMos) with participants aged 50 years or older. We considered 73 baseline features...

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Detalles Bibliográficos

Principais autores:	Sayem Borhan, Alexandra Papaioannou, Jonathan Adachi, Shrey Acharya, Suzanne N. Morin, David Goltzman, David A. Hanley, Claudie Berger, Lehana Thabane, Parminder Raina
Formato:	Artigo
Idioma:	Inglês
Publicado:	Nature Portfolio 2026-06-01
Series:	Scientific Reports
Assuntos:	Machine learning Fragility fractures Prediction XGBoost Deep learning SHAP
Acceso en liña:	https://doi.org/10.1038/s41598-026-49494-z
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