Healthcare systems have been using data mining to predict disease in recent years. Early prediction of liver diseases is important to save human life, mainly to decrease mortality rates by taking appropriate disease control measures. This paper explores early predictions of liver disease through various classification techniques. The liver disease dataset selected for this study consists of 15 CT scan images of the liver. The images were segmented with GLCM features. The main purpose of this paper is to propose a hybrid classifier algorithm for predicting liver diseases involving multiple techniques. The proposed technique is also compared with existing classifiers like Naïve Bayes, K nearest neighbor and support vector machine (SVM) on the scales of sensitivity, specificity and classification accuracy. Experimental results of the proposed hybrid classifier algorithm were found to better in predicting liver diseases.

Classification, GLCM, liver disease, prediction, standard deviation

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