Training data set optimization
CAD
CAD
Since the randomly divided training data sets also influence the classifier accuracy, we selected a group of high-performance training data sets after hundreds of cross-validation tests. The intersection of various training data sets with an accuracy of around 80 ~ 85% was selected as a standard to build the final training data set for CAD detection. The framework of the proposed model for diagnosing CAD is illustrated in Fig. Framework of the proposed model for diagnosing CAD. A stepwise workflow to optimize the predictive learning model by screening and recomposing features, selecting superior classifiers, and identifying the convergent training data set. 