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Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm.
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Abstract
We aimed to improve the diagnostic accuracy of automatic myocardial perfusion SPECT (MPS) interpretation analysis for the prediction of coronary artery disease (CAD) by integrating several quantitative perfusion and functional variables for noncorrected (NC) data by Support Vector Machine (SVM) algorithm, a computer method for machine learning.