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Published in: BMC Medical Informatics and Decision Making 1/2021

Open Access 01-12-2021 | Coronary Heart Disease | Research article

Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors

Authors: Jingyi Zhang, Huolan Zhu, Yongkai Chen, Chenguang Yang, Huimin Cheng, Yi Li, Wenxuan Zhong, Fang Wang

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

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Abstract

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 70–87.7% 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.

Conclusion

Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.
Appendix
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Metadata
Title
Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors
Authors
Jingyi Zhang
Huolan Zhu
Yongkai Chen
Chenguang Yang
Huimin Cheng
Yi Li
Wenxuan Zhong
Fang Wang
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-021-01535-5

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