Hepatitis C Disease Prediction Using Machine Learning Models With SMOTE-Based Data Balancing

Omar Shakir Hasan

doi:10.51173/ijds.v3i2.82

Authors

Omar Shakir Hasan Directorate of Educational Nineveh , Mosul, Iraq https://orcid.org/0000-0002-2509-1178

DOI:

https://doi.org/10.51173/ijds.v3i2.82

Keywords:

Hepatitis C, Machine Learning, Ensemble Learning, XGBoost, SMOTE

Abstract

The problem of hepatitis C is worldwide, with many potential health issues associated with this serious disease (e.g., hepatitis C may lead to liver cirrhosis in some patients). Accurate and early diagnosis of Hepatitis C will improve patient care and possibly lower the death rate from this virus. This paper examined the use of machine learning models to classify patients' hepatitis C stage using laboratory-based clinical data from the hepatitis C dataset on Kaggle, with n=615 subjects. The data was split with 80% for training and 20% for testing. A preprocessing methodology was implemented on the dataset to obtain the best fit for multiple models. For example, missing data were imputed using KNN; categorical variables were encoded as numbers (label encoding); and each feature was scaled (standard scaler). To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE). The models we implemented were: XGBoost, Random Forest, Decision Tree, Logistic Regression, and Gaussian Naïve Bayes. Our results show that the XGBoost/Synthetic Minority Over-sampling Technique performed best, achieving 95% classification accuracy and a macro F1 score of 0.95, compared to the other models we tested. The results provide additional evidence that combining SMOTE with ensemble learning offers a robust decision-support solution for classifying patients with Hepatitis C in a multiclass setting and an efficient means for early clinical diagnosis.

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