A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.

Journal: Computer methods and programs in biomedicine

Published Date: May 13, 2019

Abstract

BACKGROUND AND OBJECTIVE: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.

Authors

Sabrina Mezzatesta

Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy.
Claudia Torino

Institute of Clinical Physiology - Reggio Calabria Unit, Laboratory of Bioinformatics, National Research Council, Italy.
Pasquale De Meo

Department of Ancient and Modern Civilizations, University of Messina, Messina, Italy.
Giacomo Fiumara

Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy.
Antonio Vilasi

Institute of Clinical Physiology - Reggio Calabria Unit, Laboratory of Bioinformatics, National Research Council, Italy. Electronic address: avilasi@ifc.cnr.it.

Keywords

Aged Algorithms Bayes Theorem Biomarkers Cardiovascular Diseases Databases, Factual False Positive Reactions Female Humans Italy Kidney Failure, Chronic Machine Learning Male Middle Aged Models, Statistical Prognosis Registries Risk Sensitivity and Specificity Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31319965)

A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.

Abstract

Authors

Keywords

External Resources

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