XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study

XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study

Abstract Objective To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features. Methods In this retrospective, population-based cohort study, anonymized questionnair...

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Bibliographic Details
Main Authors: Zijuan Fan, Wenzhu Song, Yan Ke, Ligan Jia, Songyan Li, Jiao Jiao Li, Yuqing Zhang, Jianhao Lin, Bin Wang
Format: Article
Language:English
Published: BMC 2024-12-01
Series:Arthritis Research & Therapy
Subjects:
Knee osteoarthritis
Classification
XGBoost
Boruta
SHAP
Machine learning
Online Access:https://doi.org/10.1186/s13075-024-03450-2
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https://doi.org/10.1186/s13075-024-03450-2

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