Fig. 2From: Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics StudyAssessing the importance of predictors with Gini and Accuracy importance indices based on the implementation of the random forest model; we confirmed that BMI, physical activity, and age were the most influential variables in MetS predictionBack to article page