Bootstrapped Aggregating Optimization in Random Forest for Hepatitis Risk

MARSELINA ENDAH HISWATI; MOHAMMAD DIQI; ENDANG NURUL SYAFITRI; ANNUR FAUZIYYAH

doi:10.26623/transformatika.v22i1.9073

Authors

MARSELINA ENDAH HISWATI UNIVERSITAS RESPATI YOGYAKARTA
MOHAMMAD DIQI UNIVERSITAS RESPATI YOGYAKARTA https://orcid.org/0000-0002-9012-9080
ENDANG NURUL SYAFITRI UNIVERSITAS RESPATI YOGYAKARTA
ANNUR FAUZIYYAH UNIVERSITAS RESPATI YOGYAKARTA

DOI:

https://doi.org/10.26623/transformatika.v22i1.9073

Keywords:

Hepatitis, Random Forest, Bootstrapped Aggregating, Risk Prediction, Ensemble Techniques

Abstract

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.

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