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This paper applies ensemble machine learning models (Random Forest, Gradient Boosting, XGBoost, Extra Trees) to detect cirrhosis in hepatitis C patients using 28 features from 2038 Egyptian patients. The Extra Trees model achieved 96.92% accuracy with only 16 features, outperforming other models.