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Feature Importance — ML Prediction
Dataset
Cardiovascular risk cohort — 200 rows · 8 variables
Analysis
Random forest classification predicting CVD event. Feature importance ranked by mean decrease in impurity across 100 trees.
Output preview
HbA1c
41%
BMI
28%
Age
19%
Sex
9%
Creatinine
3%
Random forest · 100 trees · CVD event outcome
All data shown is fully synthetic and for demonstration purposes only.
Example AI interpretation
HbA1c contributes 41% of the model's predictive power, followed by BMI (28%) and age (19%). Sex and creatinine have minimal importance. This suggests that glycaemic control and body composition are the dominant predictors of cardiovascular risk in this cohort.
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