  Background: Extensive clinical evidence suggests that a preventive screening
of coronary heart disease (CHD) at an earlier stage can greatly reduce the
mortality rate. We use 64 two-dimensional speckle tracking echocardiography
(2D-STE) features and seven clinical features to predict whether one has CHD.
Methods: We develop a machine learning approach that integrates a number of
popular classification methods together by model stacking, and generalize the
traditional stacking method to a two-step stacking method to improve the
diagnostic performance. Results: By borrowing strengths from multiple
classification models through the proposed method, we improve the CHD
classification accuracy from around 70% to 87.7% on the testing set. The
sensitivity of the proposed method is 0.903 and the specificity is 0.843, with
an AUC of 0.904, which is significantly higher than those of the individual
classification models. Conclusions: Our work lays a foundation for the
deployment of speckle tracking echocardiography-based screening tools for
coronary heart disease.
