Subject: Computer and information sciences
Subject: Health sciences
Year: 2023
Type: Article
Type: PeerReviewed
Title: Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population – Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy
Author: Stojanov, Done
Author: Lazarova, Elena
Author: Veljkova, Elena
Author: Rubartelli, Paolo
Author: Giacomini, Mauro
Abstract: Totally 167 patients were admitted at cardiology ward in Villa Scassi hospital, Genoa, Italy. We worked with two control groups: heart failure 59 patients (mean age: 71.37 ± 13.27 years) and chronic-ischemic heart disease 108 patients (mean age: 68.85 ± 11.3 years). Nine parameters: Hb, Serum Creatinine, LDL, HDL, Triglycerides, ALT, AST, hs-cTnI, CRP were evaluated onset to hospitalization. We aimed to identify significant independent predictors relative to the outcome of heart failure versus chronic-ischemic heart disease and select combination of biochemical parameters in logistic regression-based model that would provide on average excellent discrimination to the outcome of heart failure versus chronic-ischemic heart disease in elderly population.
Publisher: Elsevier
Relation: https://eprints.ugd.edu.mk/31408/
Identifier: oai:eprints.ugd.edu.mk:31408
Identifier: https://eprints.ugd.edu.mk/31408/1/1-s2.0-S1018364723000356-main.pdfIdentifier: Stojanov, Done and Lazarova, Elena and Veljkova, Elena and Rubartelli, Paolo and Giacomini, Mauro (2023) Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population – Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy. Journal of King Saud University - Science, 35 (3). p. 102573. ISSN 1018-3647
Identifier: https://doi.org/10.1016/j.jksus.2023.102573
