International Conference on Computer and Knowledge Engineering

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Graph-Theoretic Approach and Advanced Data Balancing for Liver Disease Diagnosis Improvement

Authors :

Soheib Kiani¹ Sadegh Sulaimany²

1- University of Kurdistan 2- University of Kurdistan

Keywords :

Graph theory،liver disease diagnosis،data balancing،machine learning،ensemble methods

Abstract :

Liver disease diagnosis remains challenging due to asymptomatic early stages and limitations of traditional diagnostic methods. This paper presents a novel graph-theoretic approach combined with advanced data balancing for improved liver disease classification. We develop a patient similarity graph using cosine similarity of biochemical features, extracting five centrality measures (degree, clustering coefficient, betweenness, closeness, and eigenvector centrality) to capture relational patterns overlooked by conventional methods. The Indian Liver Patient Dataset is enhanced from 10 to 15 features through graph-based feature engineering. Class imbalance is addressed using SMOTEENN technique. An ensemble voting classifier incorporating multiple algorithms (XGBoost, LightGBM, Random Forest, etc.) is evaluated via 10-fold cross-validation. Our approach achieves superior performance with 95.74% accuracy, 94.25% precision, 99.31% recall, 96.67% F1-score, and 98.53% ROC AUC, significantly outperforming existing methods. Results demonstrate that leveraging patient relationships through graph-based features substantially enhances diagnosis accuracy.

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