Congestive heart failure detection using random forest classifier

By Zerina Mašetić, Abdulhamit Subasi. Comput. Methods Programs Biomed., 2016

303 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Health Information Management, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/4e681b863bf4a1e9d7651cbc6815801cfaa2d97d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Jeroen J. Bax
2. James K. Min

Citations in the corpus

• Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques (2021, methodology): “The RF classifier (describer below) may perform better [74], for heart disease detection or alternatively the combining strategy using bagged decision trees [75].”
• Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, methodology): “76,77 83 For example, Yang et al. proposed a two-layer binary SVM classification scheme trained on clinical parameters from 289 patients, in which individuals were first either diagnosed with heart failure or, if the diagnosis was uncertain, relegated to a secondary triage.”