Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery
Wenji Yu1, Le Yang1, Feifei Zhang1
1Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China.
Insights
An explainable machine learning model effectively screens for hemodynamically significant coronary artery disease (CAD) using traditional risk factors, coronary artery calcium (CAC), and epicardial fat volume (EFV). This approach provides personalized risk predictions with high accuracy.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning
Background:
- Coronary artery disease (CAD) diagnosis relies on invasive procedures.
- Non-invasive imaging like CT scans offer potential for early screening.
- Explainable AI can enhance the interpretability of predictive models.
Purpose of the Study:
- To develop an explainable machine learning (ML) model for screening hemodynamically significant CAD.
- To integrate traditional risk factors, coronary artery calcium (CAC), and epicardial fat volume (EFV) into the ML model.
- To provide personalized risk predictions with transparent explanations.
Main Methods:
- Utilized data from 184 symptomatic inpatients undergoing SPECT/MPI and ICA.
- Collected clinical data, CAC, and EFV from non-contrast CT scans.
- Employed recursive feature elimination (RFE) and XGBoost classifier, validated with SHapley Additive exPlanations (SHAP).
Main Results:
- XGBoost model achieved an AUC of 0.89 in the test cohort.
- Key predictors identified were EFV, CAC, diabetes mellitus, hypertension, and hyperlipidemia.
- The model demonstrated high sensitivity (68.0%), specificity (96.8%), and accuracy (83.9%).
Conclusions:
- An explainable ML model integrating EFV and CAC shows promise for non-invasively assessing hemodynamically significant CAD.
- The model provides accurate and interpretable risk predictions, aiding clinical decision-making.
- ML combined with SHAP offers transparent, personalized risk assessment for CAD.
Abstract:
We sought to establish an explainable machine learning (ML) model to screen for hemodynamically significant coronary artery disease (CAD) based on traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV) measured from non-contrast CT scans. 184 symptomatic inpatients who underwent Single Photon Emission Computed Tomography/Myocardial Perfusion Imaging (SPECT/MPI) and Invasive Coronary Angiography (ICA) were enrolled. Clinical and imaging features (CAC and EFV) were collected. Hemodynamically significant CAD was defined when coronary stenosis severity ≥ 50% with a matched reversible perfusion defect in SPECT/MPI. Data was randomly split into a training cohort (70%) on which five-fold cross-validation was done and a test cohort (30%). The normalized training phase was preceded by the selection of features using recursive feature elimination (RFE). Three ML classifiers (LR, SVM, and XGBoost) were used to construct and choose the best predictive model for hemodynamically significant CAD. An explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanation of the model's decision. In the training cohort, hemodynamically significant CAD patients had significantly higher age, BMI and EFV, higher proportions of hypertension and CAC comparing with controls (P all < .05). In the test cohorts, hemodynamically significant CAD had significantly higher EFV and higher proportion of CAC. EFV, CAC, diabetes mellitus (DM), hypertension, and hyperlipidemia were the highest ranking features by RFE. XGBoost produced better performance (AUC of 0.88) compared with traditional LR model (AUC of 0.82) and SVM (AUC of 0.82) in the training cohort. Decision Curve Analysis (DCA) demonstrated that XGBoost model had the highest Net Benefit index. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 0.89, 68.0%, 96.8%, 94.4%, 79.0% and 83.9% in the XGBoost model. A XGBoost model based on EFV, CAC, hypertension, DM and hyperlipidemia to assess hemodynamically significant CAD was constructed and validated, which showed favorable predictive value. ML combined with SHAP can offer a transparent explanation of personalized risk prediction, enabling physicians to gain an intuitive understanding of the impact of key features in the model.
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