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

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

SUBMITTER: Zhang J 

PROVIDER: S-EPMC8196502 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature