StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection

2025-07-31

AI Summary

Existing liver disease classification models suffer from high misclassification rates, poor interpretability, substantial computational overhead, and inadequate preprocessing. To address these challenges, this paper proposes StackLiverNet—a clinically oriented, interpretable stacked ensemble framework. It integrates random undersampling to mitigate class imbalance, recursive feature elimination for optimal feature subset selection, and a hyperparameter-optimized LightGBM meta-learner to aggregate multiple base classifiers. Innovatively, it incorporates LIME and SHAP for both local and global interpretability, and employs Morris sensitivity analysis to validate the clinical credibility of key biomarkers. Evaluated on a public dataset, StackLiverNet achieves 99.89% accuracy, a Cohen’s Kappa of 0.9974, and an AUC of 0.9993, with only five misclassifications. Training and inference times are merely 4.28 seconds and 0.11 seconds, respectively—demonstrating exceptional precision, robustness, and real-time deployability.

Technology Category

• Machine Learning: Ensemble Methods
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Liver diseases are a serious health concern in the world, which requires precise and timely diagnosis to enhance the survival chances of patients. The current literature implemented numerous machine learning and deep learning models to classify liver diseases, but most of them had some issues like high misclassification error, poor interpretability, prohibitive computational expense, and lack of good preprocessing strategies. In order to address these drawbacks, we introduced StackLiverNet in this study; an interpretable stacked ensemble model tailored to the liver disease detection task. The framework uses advanced data preprocessing and feature selection technique to increase model robustness and predictive ability. Random undersampling is performed to deal with class imbalance and make the training balanced. StackLiverNet is an ensemble of several hyperparameter-optimized base classifiers, whose complementary advantages are used through a LightGBM meta-model. The provided model demonstrates excellent performance, with the testing accuracy of 99.89%, Cohen Kappa of 0.9974, and AUC of 0.9993, having only 5 misclassifications, and efficient training and inference speeds that are amenable to clinical practice (training time 4.2783 seconds, inference time 0.1106 seconds). Besides, Local Interpretable Model-Agnostic Explanations (LIME) are applied to generate transparent explanations of individual predictions, revealing high concentrations of Alkaline Phosphatase and moderate SGOT as important observations of liver disease. Also, SHAP was used to rank features by their global contribution to predictions, while the Morris method confirmed the most influential features through sensitivity analysis.

Problem

• Improving liver disease detection accuracy and interpretability
• Addressing class imbalance and computational inefficiency issues
• Enhancing model robustness with advanced preprocessing techniques

Innovation

• Stacked ensemble model for liver disease detection
• Advanced preprocessing and feature selection techniques
• Interpretable predictions using LIME and SHAP