StackLiverNet, a stacked XGBoost/KNN/LightGBM ensemble, reports 99.89% test accuracy on the Kaggle Liver Disease Patient Dataset, with feature selection performed before the train/test split.
Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches,
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StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection
StackLiverNet, a stacked XGBoost/KNN/LightGBM ensemble, reports 99.89% test accuracy on the Kaggle Liver Disease Patient Dataset, with feature selection performed before the train/test split.