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Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach.

Chia-Lin LeeWei-Ju LiuShang-Feng Tsai
Published in: Nutrients (2022)
This was the first study using ML to predict IR in patients with CKD. Our results showed that the RF algorithm had the best AUC of ROC and the best SHAP value differentiation. This was also the first study that included both macronutrients and micronutrients. We concluded that ML algorithms, particularly RF, can help determine risk factors and predict IR in patients with CKD.
Keyphrases
  • chronic kidney disease
  • machine learning
  • end stage renal disease
  • risk factors
  • insulin resistance
  • deep learning
  • artificial intelligence
  • big data
  • type diabetes
  • adipose tissue
  • metabolic syndrome
  • glycemic control