Why the study?
Clinical decision support systems are widely used in cardiac care due to disease complexity, but the most common variables and machine learning techniques used to build them needed identification.
Logistic regression and support vector machines, utilizing variables like vital signs and clinical scores (SAPS, SOFA, APACHE), are the most common approaches for building machine learning-based clinical decision support systems in cardiac care.
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Logistic regression and SVM warrant priority in cardiac CDSS; reinforces their dominance while standardizing 22 variables and scores for future validation.
Rahman et al. (2019) studied this question.
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