Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning
- Medical Imaging Research Center, Illinois Institute of Technology (United States)
- Cedars-Sinai Medical Center, Departments of Imaging and Medicine (United States)
Background: We developed machine-learning (ML) models to estimate a patient’s risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient’s risk to provide insight to clinicians beyond that of a “black box.” Methods: We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model’s rationale to facilitate clinical interpretation. Results: The baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy. Conclusions: LASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.
- OSTI ID:
- 22961954
- Journal Information:
- Journal of Nuclear Cardiology (Online), Vol. 26, Issue 5; Other Information: Copyright (c) 2019 American Society of Nuclear Cardiology; Country of input: International Atomic Energy Agency (IAEA); ISSN 1532-6551
- Country of Publication:
- United States
- Language:
- English
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