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Abstract
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This study, titled “An Efficient Ensemble Learning-Based Method for Liver Disease Prediction,” focuses on developing a robust machine learning framework using advanced ensemble techniques such as Random Forest, XGBoost, and stacking to improve the accuracy and reliability of liver disease diagnostics. By leveraging diverse clinical datasets, including liver enzyme levels and protein counts, the research emphasizes feature selection and preprocessing methods like Recursive Feature Elimination and data balancing to optimize model performance. Key metrics such as accuracy, precision, recall, and AUC-ROC guide the evaluation of the proposed model, ensuring its applicability in real-world healthcare scenarios. The study also explores integrating additional data types, such as imaging and genomic data, to provide a multi-modal predictive approach. With an emphasis on computational efficiency and interpretability through explainable AI tools like SHAP and LIME, this research aims to advance the early detection, risk stratification, and management of liver diseases, offering practical solutions for improving patient outcomes and healthcare delivery systems.
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