File(s) not publicly available
GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis
journal contribution
posted on 2022-02-01, 00:00 authored by Javad Hassannataj Joloudari, Faezeh Azizi, Mohammad Ali Nematollahi, Roohallah AlizadehsaniRoohallah Alizadehsani, Edris Hassannatajjeloudari, Issa Nodehi, Amir MosaviBackgroundCoronary 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.MethodsHence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset.ResultsAs a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset.ConclusionWe demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.
History
Journal
Frontiers in cardiovascular medicineVolume
8Article number
ARTN 760178Pagination
1 - 14Publisher
Frontiers / Frontiers Media / Frontiers Research FoundationLocation
Lausanne, SwitzerlandPublisher DOI
Link to full text
ISSN
2297-055XeISSN
2297-055XLanguage
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC