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GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset
[article]
2021
arXiv
pre-print
Coronary heart 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. Hence, this paper provides a new hybrid machine learning model called Genetic Support Vector Machine and Analysis of Variance (GSVMA). The
arXiv:2108.08292v1
fatcat:q2c7fky7fzhdljve5bs43b7guu