GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis
Hassannataj Joloudari Javad; Azizi Faezeh; Nematollahi Mohammad Ali; Alizadehsani Roohallah; Hassannatajjeloudari Edris; Nodehi Issa; Mosavi Amir
DOI : 10.3389/fcvm.2021.760178
MTMT : 32647459
Megjelenés dátuma : 2022
Folyóirat címe : Frontiers In Cardiovascular Medicine
Évfolyam : 8
Oldalszám : 14.jan
Dokumentum típusa : folyóiratcikk
Kulcsszó : coronary artery disease, genetic algorithm, support vector machine, machine learning, diagnosis, Orvostudományok, Elméleti orvostudományok
Absztrakt :
Background: Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis.
