Predicting coronary stenosis progression using plaque fatigue from ivus-based thin-slice models: a machine learning random forest approach

HIGHLIGHTS

• who: Xiaoya Guo and collaborators from the Shanghai Jiao Tong University, China have published the article: Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach, in the Journal: (JOURNAL) of 04/04/2022
• what: The authors used RF algorithm with optimal performance on the dataset as the prediction method, employed change of lumen area as the measurement of stenosis progression, added plaque fatigue as a class of predictor, the evaluation of the effect of RF parameters is shown in Figures 2, 3.
• how . . .
  

   

  

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