Machine learning selection of 51 multimodal predictors improved incident CAD discrimination (C-statistic 0.796) compared to Framingham, pooled cohort equations, and QRISK3 (0.754-0.761).
Does a machine learning model using 51 selected multimodal features improve discrimination of incident CAD compared to standard risk scores?
A machine learning approach selecting 51 multimodal features improved the prediction of incident coronary artery disease compared to traditional clinical risk scores.
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improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754-0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.
Agrawal et al. (Mon,) reported a other. Machine learning selection of 51 multimodal predictors improved incident CAD discrimination (C-statistic 0.796) compared to Framingham, pooled cohort equations, and QRISK3 (0.754-0.761).