Development of heart failure risk prediction models based on a multi-marker approach using random forest algorithms

Abstract

The random forest algorithm provides a robust method to assess the accuracy and importance of predictor variables. The combination of CK-MB, BNP, Gal-3, and sST2 achieves improvement in prediction accuracy for HF.