[Retracted] An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Table 6

Shows the experimental results of the optimized random forest model.

Criterion	Max depth	Max features	N Estimators	Min samples leaf	Accuracy
Gini	70	0	0	0	85%
Entropy	60	Auto	0	0	86%
Gini	50	Auto	100	0	87%
Entropy	80	Auto	100	100	73%
Gini	100	Auto	100	50	76%
Entropy	30	0	80	60	80%
Gini	40	0	90	40	78%
Gini	25	Auto	70	30	75%
Entropy	20	Auto	40	25	82%
Entropy	35	Auto	30	20	81%
Gini	45	0	60	35	80%