Diagnosis of liver disease principally depends on physician's subjective knowledge. Automatic prediction of the disease is a critical real-world medical problem. This work presents an EHC-ERF-based intelligence-integrated model purposive to predict different types of liver disease including alcoholic liver damage, primary hepatoma, liver cirrhosis, and cholelithiasis. These diseases cause many clinical complications, and their accurate assessment is the only way for providing efficient treatment facilities to patients. EHC is deployed to divide the data into a hierarchy structure that is more informative for the disease predictions carried out by ERF. The occurrence of ERF error rate was dependent on correlation and strength of each individual tree where correlation is directly proportional to forest error rate and strength is inversely proportional to the forest rate. In total, two individual and three integrated classification models are developed to achieve enhanced predictions for the liver disease types. Analysis of results showed that the proposed framework achieved better outcomes in terms of accuracy, true positive rate, precision, F-measure, kappa statistic, mean absolute error, and root mean squared error. Furthermore, it achieved the highest accuracy rates when compared with the state-of-the-art techniques. Results also indicated that the weighted distance function employed in EHC has improved the efficiency of proposed system and has shown the capability to be used by physicians for diagnostic advice.