Optimization of Heart Failure Risk Prediction Using Random Forest Classifier Algorithm
DOI:
https://doi.org/10.62205/mjgcs.v2i2.105Keywords:
Optimization, Prediction, Heart Failure, Algorithm, Random Forest ClassifierAbstract
This study discusses the optimization of heart failure prediction using the Random Forest Classifier algorithm with a focus on feature selection marked by a threshold and the number of features used. The results of the analysis show that the right threshold has a significant effect on model performance. At a threshold of 0.02, the model achieves the best performance with the highest accuracy, precision, and F1-score values. However, increasing the threshold above 0.08 causes a gradual decrease in model performance. In addition, the number of features used also affects the prediction results, where the right combination of features can increase the effectiveness of the classification. Therefore, this study emphasizes the importance of optimizing thresholds and feature selection in building more accurate and efficient prediction models.
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