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Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Table 3

Top 10 risk factor variables for CVD algorithms listed in descending order of coefficient effect size (ACC/AHA; logistic regression), weighting (neural networks), or selection frequency (random forest, gradient boosting machines).

Algorithms were derived from training cohort of 295,267 patients.

Table 3