ArticleFrontiers in cardiovascular medicine2021
GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 30 citations in OpenAlex.
- Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data.Journal of diabetes and metabolic disorders · 2025Article
- Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection.Frontiers in network physiology · 2025Article
- Altered dynamic neural activities in individuals with obsessive-compulsive disorder and comorbid depressive symptoms.Frontiers in psychiatry · 2024Article
- CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.Frontiers in public health · 2023Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
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Authors and funding
7 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary 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.
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