Data augmentation alters feature importance in XGBoost for CVD prediction
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Data augmentation has an impact on feature importance in XGBoost, a method used for predicting cardiovascular disease. The alteration of feature importance suggests that data augmentation can change the way the model weighs different factors when making predictions. The effect of this alteration on the overall prediction of cardiovascular disease is not specified.