Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach
Marzieh Ketabi,
Aref Andishgar,
Zhila Fereidouni
et al.
Abstract:BackgroundHeart failure (HF) is a global problem, affecting more than 26 million people worldwide. This study evaluated the performance of 10 machine learning (ML) algorithms and chose the best algorithm to predict mortality and readmission of HF patients by using The Fasa Registry on Systolic HF (FaRSH) database.HypothesisML algorithms may better identify patients at increased risk of HF readmission or death with demographic and clinical data.MethodsThrough comprehensive evaluation, the best‐performing model … Show more
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