Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death-Reference-Cited by-同舟云学术

Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death

Published:2019-06-26 Issue:6 Volume:14 Page:e0218760
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Awan Saqib E.,Bennamoun Mohammed,Sohel Ferdous^ORCID,Sanfilippo Frank M.,Chow Benjamin J.,Dwivedi Girish^ORCID

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference28 articles.

1. Global public health burden of heart failure;G Savarese;Cardiac failure review,2017

2. The Hospital Readmissions Reduction Program—learning from failure of a healthcare policy;A Gupta;European journal of heart failure,2018

3. Predictors of readmission among elderly survivors of admission with heart failure;HM Krumholz;American heart journal,2000

4. Machine learning in heart failure: ready for prime time;SE Awan;Current opinion in cardiology,2018

5. Machine learning‐based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics;SE Awan;ESC Heart Failure,2019

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