Bootstrapped Aggregating Optimization in Random Forest for Hepatitis Risk - HISWATI, MARSELINA ENDAH; DIQI, MOHAMMAD; SYAFITRI, ENDANG NURUL; FAUZIYYAH, ANNUR (Jurnal Transformatika, 2024)
This research optimizes the Random Forest model with Bootstrapped Aggregating to predict hepatitis risk. The global significance of hepatitis as a health problem is underscored by its widespread impact. Using a Kaggle dataset comprising 596 records and 20 attributes, including age categories and gender, the study identifies limitations in predicting hepatitis risk. Through hyperparameter optimization, such as adjusting the number and depth of trees, the Random Forest model with bootstrapped aggregate achieves an accuracy of 96%, surpassing the standard model's 88%. The results demonstrate a significant improvement in precision, recall, and f1 score, particularly in reducing false negatives. The conclusion highlights the practical potential of this model for a more accurate assessment of hepatitis risk. While acknowledging limitations related to the size of the dataset, these findings provide a foundation for developing predictive models in the context of hepatitis risk, emphasizing the importance of employing ensemble techniques to improve model performance.