Machine learning models for predicting steroid-resistant of nephrotic syndrome

HIGHLIGHTS

• who: Qing Ye from the Giannina Gaslini Institute (IRCCS), Italy University Children`s Hospital in Krakow have published the Article: Machine learning models for predicting steroid-resistant of nephrotic syndrome, in the Journal: (JOURNAL)
• what: The authors aim to build an SVM model using part of these 87 clinical variables to predict SSNS or SRNS. The authors use the leave-one-out cross-validation (LOOCV) accuracy and the following measurements to show the performance of different models. The authors propose a statistical test procedure for overfitting with the null hypothesis that the model is overfitted . . .
  

   

  

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