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Hepatitis disease is a deadliest disease. The management and diagnosis of hepatitis disease is expensive and requires high level of human expertise which poses challenges for the health care system in underdeveloped and developing countries. Hence, development of automated methods for accurate prediction of hepatitis disease is inevitable. In this paper, we develop a diagnostic system which hybridizes a linear support vector machine (SVM) model with adaptive boosting (AdaBoost) model. We exploit sparsity in linear SVM that is caused by L₁ regularization. The sparse L₁‐regularized SVM is capable of eliminating redundant or irrelevant features from feature space. After filtering features through the sparse linear SVM, the output of the SVM is applied to the AdaBoost ensemble model which is used for classification purposes. Two types of numerical experiments are performed on the clinical features of …