An Ensemble Approach for Artificial Neural Network-Based Liver Disease Identification from Optimal Features through Hybrid Modeling Integrated with Advanced Explainable AI

Abstract

Liver disease is any condition that negatively affects the liver's function or structure, resulting in impaired liver function and various health complications. Abnormal conditions are rapidly increasing day by day. In this study, we used a dataset of key liver disease-related blood sample biomarkers to utilize various Machine learning (ML) techniques to enhance the accuracy of liver disease prediction. Specifically, we integrated the artificial neural network (ANN) model with five ML models: Stacked Generalization (Stacking), Bootstrap Aggregating (Bagging), Adaptive Boosting (AdaBoost), Gradient-Boosted Decision Tree (GBDT), and Support Vector Machine (SVM)—resulting in five distinct hybrid models: Stacking with ANN (SANN), Bagging with ANN, AdaBoost with ANN (ABANN), GBDT with ANN (GANN), and SVM with ANN (SVMANN). We tested all these hybrid models with feature selection techniques, including linear discriminant analysis (LDA), principal component analysis (PCA), recursive feature elimination (RFE), and also without feature selection. Through extensive testing, we found that these five hybrid models performed best when combined with LDA rather than PCA, RFE, or no feature selection. This discovery led us to create a max voting ensemble (MVE) of these LDA-optimized hybrid models. Remarkably, our prediction accuracy increased from 79.15% to 98.38% using the MVE. Furthermore, we employ Explainable Artificial Intelligence techniques such as Local Interpretable Model-agnostic Explanations, Shapley Additive Explanations, and Individual Conditional Expectations to analyze and enhance trust in the predictions. We also implemented 10-fold cross-validation to ensure the robustness and reliability of our results. This research underscores the significance of advancements in neural network systems and highlights the potential for hybrid models to improve predictive accuracy in liver disease diagnosis. Our findings pave the way for a new generation of computational technologies endowed with intelligence, ultimately contributing to better health outcomes and a deeper understanding of liver disease dynamics.

Received: 6 November 2024 | Revised: 11 February 2025 | Accepted: 3 March 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data used in this study will be accessible upon request to the corresponding author.

Author Contribution Statement

Safiul Haque Chowdhury: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Mamun: Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Md. Tanvir Ahmed Shaikat: Visualization, Project administration. Mohammed Ibrahim Hussain: Writing – review & editing, Supervision. MD. Sadiq Iqbal: Writing – review & editing, Visualization, Supervision. Muhammad Minoar Hossain: Writing – review & editing, Supervision.