Key result
Five-factor non-laboratory model achieves ~74% accuracy for heart disease detection, comparable to comprehensive laboratory models.
Why the study?
Does a non-invasive risk prediction model using simple clinical factors accurately detect heart disease compared to comprehensive models in patients referred for coronary angiography?
Does a non-invasive risk prediction model using simple clinical factors accurately detect heart disease compared to comprehensive models in patients referred for coronary angiography?
A simple non-invasive risk prediction model without laboratory data can achieve 72% accuracy in detecting heart disease, approaching the 76% accuracy of comprehensive models.
Developing a heart disease detection model using simple non-laboratory risk factors plays an important role in preventive care, especially for high risk subjects. The model allows physicians/epidemiologists to effectively diagnose a person as having heart disease. In this work, we aim to develop a non-invasive risk prediction model for automated heart disease detection that involves age, gender, rest blood pressure, maximum heart rate, and rest electrocardiography. We examine four public datasets from 1071 participants who were referred for a special X-ray of the heart's arteries (i.e., to see if they are narrowed or blocked). The subjects also undertook a physical examination and three non-invasive tests. To estimate the heart disease status, we apply a generalized linear model with regularization paths via coordinate descent. Even without laboratory-based data (e.g., serum cholesterol, fasting blood sugar), we observed a prediction accuracy as high as 72%, compared with 76% of other comprehensive models. This observation suggests that few non-invasive factors utilizing recent advances in data analytics can replace the current practices of heart disease risk assessment.
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Mai et al. (2018) studied Heart disease (n=1,071). Non-laboratory-based risk prediction model (GLM) vs. Comprehensive models including laboratory-based data was evaluated on Prediction accuracy for heart disease detection (Hold-out Test Ridge model) (95% CI 0.6566-0.8131, p=< 0.0001). A generalized linear model using five non-laboratory risk factors achieved a prediction accuracy of 74.05% for automated heart disease detection, comparable to comprehensive models requiring laboratory data.
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