Hyperparameter Optimization of Random Forest Algorithm Technique in Acute Coronary Syndrome Cases

Abstract

Research using random forest hyperparameter optimization in the case of acute coronary syndrome allows us to obtain a more optimal prediction model, but we can find a gap assumption where the learning carried out by the model still shows symptoms of over-fitting, characterized by a fairly large gap between the training and cross-training processes. validation in the model evaluation process. The research that will be carried out will provide a more optimal prediction model and will not produce symptoms of overfitting of the model using optimization techniques for the hyperparameters in the random forest algorithm. After carrying out various scenarios and testing accuracy, precision scores and various combinations of hyperparameters in the random forest algorithm, it was concluded that the model with the best optimization had a split ratio of 90:10 with an accuracy level of 84.44%, a precision score of 85, 29% and an MSE score of 0.1556 with the results of a combination of random forest optimization hyperparameters using gridCV. The optimization model using random grid cross validation that was built succeeded in reducing the level of over-fitting in the data, decreasing the MSE (mean squared error) from 0.17 and 0.24 to 0.15 for each model.