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

Sayem Borhan; Alexandra Papaioannou; Jonathan Adachi; Shrey Acharya; Suzanne N. Morin; David Goltzman; David A. Hanley; Claudie Berger; Lehana Thabane; Parminder Raina

doi:10.1038/s41598-026-49494-z

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

S Sayem Borhan A Alexandra Papaioannou J Jonathan Adachi S Shrey Acharya S Suzanne N. Morin D David Goltzman D David A. Hanley C Claudie Berger L Lehana Thabane P Parminder Raina

Download PDF View Original

APA MLA Chicago BibTeX RIS

Article Details

Journal Scientific Reports

Volume / Issue Vol. 16, Issue 1

Published June 02, 2026

DOI 10.1038/s41598-026-49494-z

ISSN 2045-2322

Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (10)

S

Sayem Borhan

A

Alexandra Papaioannou

J

Jonathan Adachi

S

Shrey Acharya

S

Suzanne N. Morin

D

David Goltzman

D

David A. Hanley

C

Claudie Berger

L

Lehana Thabane

P

Parminder Raina

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

Article Details

Journal Info

Authors (10)

Related Articles from this Journal

AI models of unstable flow exhibit hallucination

The MCAA-YOLO + XPBI integrated model provides a hybrid intelligent measurement method for predicting the body weight of Hu sheep

Application of iron nano-biofertilizers as a sustainable approach to enhance growth and micronutrient uptake in soybean

Adolescents’ status-dependent public and private conformity to prosocial and antisocial decisions in a public goods game

An explainable hierarchical capsule network framework for supporting expert sensory evaluation of herbal medicines with natural language generation