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Mining meaningful information from huge medical datasets is a key aspect of automated disease diagnosis. In recent years, liver disease has emerged as one of the commonly occurring diseases across the world. In this paper, a Convolutional Neural Network (CNN) based model is proposed for the identification of liver disease. Furthermore, the performance of CNN was also compared with traditional machine learning approaches, which include Naive Bayes (NB), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). For evaluation, two datasets were used: BUPA and ILPD. The experimental results showed that CNN was effective for the classification of liver disease, which produced an accuracy of 75.55%, and 72.00% on the BUPA and ILPD datasets, respectively.