The implementation of the Random Forest Algorithm with Resampling and Without Resampling on the Hepatitis C Disease Dataset

I Gede Hendrayana; Ni Putu Dita Ariani Sukma Dewi; Jiyestha Aji Dharma Aryasa; I Made Ade Prayoga; Rizki Anom Raharjo

doi:10.47709/cnahpc.v7i3.6089

Authors

I Gede Hendrayana Institut Teknologi dan Kesehatan Bali, Indonesia
Ni Putu Dita Ariani Sukma Dewi Institut Teknologi dan Kesehatan Bali, Indonesia
Jiyestha Aji Dharma Aryasa Institut Teknologi dan Kesehatan Bali, Indonesia
I Made Ade Prayoga Institut Teknologi dan Kesehatan Bali, Indonesia
Rizki Anom Raharjo Institut Teknologi dan Kesehatan Bali, Indonesia

DOI:

https://doi.org/10.47709/cnahpc.v7i3.6089

Keywords:

Hepatitis C, Random Forest, Oversampling, Undersampling

Abstract

This study evaluates the performance of Random Forest models for Hepatitis C classification using a dataset from Kaggle, focusing on addressing class imbalance through resampling techniques. We compare three approaches: baseline Random Forest without resampling, Random Forest with SMOTE+ENN (Synthetic Minority Oversampling Technique + Edited Nearest Neighbors), and Random Forest with SMOTE+OSS (Synthetic Minority Oversampling Technique + One-Sided Selection). Results show that the baseline model achieved high accuracy (0.9837) but failed to detect minority classes (e.g., suspect Blood Donor recall=0.00). SMOTE+ENN significantly improved performance, achieving perfect classification (precision=1.00, recall=1.00) for Hepatitis, Fibrosis, and Cirrhosis, while maintaining high accuracy (0.9919) and ROC AUC (0.9999). In contrast, SMOTE+OSS showed limitations in detecting Hepatitis (recall=0.00) and yielded lower precision for Fibrosis (0.44), indicating its undersampling approach may be too aggressive. The study highlights SMOTE+ENN as the most effective method for balancing class distribution and enhancing model robustness in medical diagnostics. These findings underscore the importance of selecting appropriate resampling techniques to improve minority class detection in imbalanced datasets, with implications for developing reliable AI-based diagnostic tools for Hepatitis C.

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