Liver cancer remains a major global health challenge, and hepatocellular carcinoma (HCC) accounts for approximately 80% of all primary malignant liver tumors. Among various risk factors for HCC, chronic hepatitis B (CHB) plays a particularly significant role in Asian populations, where infected individuals face a substantially higher risk than the general population. This study focuses on a Korean cohort of patients with CHB and aims to develop precise risk prediction models for HCC. Traditional clinical risk scoring systems are widely used to estimate HCC risk, but these methods have inherent limitations due to their linear structure and limited capacity to capture complex patient characteristics. To address these limitations, recent advances in predictive models and artificial intelligence have enabled more accurate and personalized disease risk predictions. This study applies a transformer-based predictive model, specifically ExcelFormer, to estimate the 5-year risk of HCC development in patients with CHB in this cohort. ExcelFormer incorporates various clinical variables and captures nonlinear interactions among features, which leads to improved predictive performance. The model achieves an AUROC of 0.8524, and its performance further improves to 0.8839 when additional laboratory variables are included. These results demonstrate clear advantages over conventional scoring systems in predictive accuracy and adaptability.