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Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost.

Kui SuXin YuanYukai HuangQian YuanMinghui YangJianwu SunShuyi LiXinyi LongLang LiuTianwang LiZhengqiang Yuan
Published in: Indian journal of orthopaedics (2023)
We demonstrate that the XGBoost is the best model for the prediction of KOA severity in the six examined models. In addition, 20 risk features were determined as the essential predictors of KOA, including the physical exam, knee symptoms/risk factors and subject characteristics, which may be useful for the identification of high-risk KOA cases and for making appropriate treatment decisions as well.
Keyphrases
  • knee osteoarthritis
  • machine learning
  • risk factors
  • physical activity
  • total knee arthroplasty
  • mental health
  • artificial intelligence
  • deep learning
  • depressive symptoms