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Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models

Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models

Therefore, in Model 3, we treated ASCVD risk as a dichotomous variable and built a logistic regression model to distinguish the high-risk category using language compared to low ASCVD scores (ie, 10%) or low-risk ( The multiclass logistic regression model on Facebook posts was trained to classify patients in four different categories ( Area under the curve (AUC) scores for each category of atherosclerotic cardiovascular disease risk scores from Model 1.

Anietie U Andy, Sharath C Guntuku, Srinath Adusumalli, David A Asch, Peter W Groeneveld, Lyle H Ungar, Raina M Merchant

JMIR Cardio 2021;5(1):e24473