Attention-Based Deep Learning Framework for Multi-Class Classification of Liver Cirrhosis Stages Using Clinical and Biochemical Biomarkers

Patti. Kavya1, Dr.G.Thirupati 2

1M.TECH Student, Department Of CSE, SVS Group of Institutions (Autonomous), Bheemaram, Hanmakonda, Telangana

2Assistant Professor, Department Of CSE, SVS Group of Institutions (Autonomous), Bheemaram, Hanmakonda, Telangana

ABSTRACT

liver cirrhosis staging is crucial to enhance prognosis among patients and optimize the therapeutic approach. An attention-based deep learning framework for automated cirrhosis stage classification using routinely collected clinical and biochemical features As solution 1: A dataset of 25,000 patient records with 19 medical attributeswas prepared after preprocessing missing-value imputation with random-forest, label encoding, and feature standardization and development model. By applying dense neural layers combined with a custom attention mechanism, we get an architecture capable of highlighting the most discriminative features while diminishing less informative patterns. On an independent test set, predictive performance (accuracy = 90.46%, Weighted F1-Score = 0.9048), and multi-class ROC analysis (AUC range = 0.970–0.984 for all stages) demonstrated that we possess high predictive power. A 5-fold cross-validation procedure further validated the method with a mean accuracy of 0.9124 ± 0.0032 demonstrating robustness and generalizability. Conclusion These results suggest that the attention model proposed is a strong, interpretable, and clinically applicable model for classifying liver cirrhosis stages and may be applied to computer-aided diagnosis systems and clinician-targeted risk stratification of patients.

Keywords: Liver Cirrhosis Classification, Attention-Based Deep Learning, Clinical Biomarkers Analysis, Computer-Aided Diagnosis