Liver cancer, the fifth most common cancer and second leading cause of cancer-related death among men worldwide, is plagued by not only lack of clinical research, but informatics tools for early detection. Consequently, it presents a major health and cost burden. Among the different types of liver cancer, hepatocellular carcinoma (HCC) is the most common and deadly form, arising from underlying liver disease. Current models for predicting risk of HCC and liver disease are limited to clinical data. A domain analysis of existing research related to screening for HCC and liver disease suggests that metabolic syndrome (MetS) may present oppportunites to detect early signs of liver disease. The purpose of this paper is to (i) provide a domain analysis of the relationship between HCC, liver disease, and metabolic syndrome, (ii) a review of the current disparate sources of data available for MetS diagnosis, and (iii) recommend informatics solutions for the diagnosis of MetS from available administrative (Biometrics, PHA, claims) and laboratory data, towards early prediction of liver disease. Our domain analysis and recommendations incorporate best practices to make meaningful use of available data with the goal of reducing cost associated with liver disease.