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 (Frontiers in Cardiovascular Medicine, 2022)
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. Results: As 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 cross-validation technique with 35 selected features on the Z-Alizadeh Sani dataset. Conclusion: To the best of our knowledge, the genetic optimization algorithm is very effective for improving accuracy. Therefore, the study confirms that the computer-aided GSVMA method outperforms other methods and it can be helped clinicians with CAD diagnosis.