Advances in application of machine learning in heart failure with preserved ejection fraction
CUI Pei-jie
LIU Yong-ming
GAO Wen-long
Abstract：This review compares the advantages and disadvantages of various machine learning(ML)methods used in studies of heart failure with preserved ejection fraction(HFpEF)and summarizes their applications in diagnosis,prediction,treatment,pathogenesis and phenotypic classification in order to identify the current research hotspots and future directions and to provide a reference for selecting appropriate ML methods in related research.
Keywords：Machine learningHeart failure with preserved ejection fractionDiagnosisPredictionPhenotypic classification
Publication Date：2025-06-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 519-524 )