Enhancing generalization in a kawasaki disease prediction model using data augmentation: cross-validation of patients from two major hospitals in taiwan

HIGHLIGHTS

• What: The model described in the JAMA journal`s experiences Model Collapse during generalizability testing. The aim of this paper is to address the persistent issue of data imbalance in medical disease datasets, exemplified by Kawasaki Disease (KD). For this research, the authors identified 22 features spanning basic patient demographics, blood tests, and urine tests, as shown in Table 1. This study designs DC and CTGAN-DC frameworks based on Ensemble Learning, oversampling, and stacking principles.
• Who: Chuan-Sheng Hung and collaborators from the from two major hospitals in Taiwan , Department of Computer Science and . . .
  

   

  

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