Multiclass classification of liver cirrhosis stages using clinical data. Compares Random Forest vs. XGBoost with SMOTE for class imbalance and hyperparameter tuning for optimization. Aims to support clinical decision-making.
This repository addresses the complex task of multi-class prediction for cirrhosis outcomes, a chronic liver condition characterized by tissue damage. Focused on forecasting diverse outcomes such as severity levels and disease stages, the model holds significance in enhancing personalized healthcare for cirrhosis patients.
Prediction of Cirrhosis
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