Classification and Rule Extraction using Rough Set for Diagnosis of Liver Disease and its Types
Sajja Karthik, A. Priyadarishini, J. Anuradha, Bala Krushna Tripathy · 2011
The liver supports almost every organ in the body and is vital for our survival. Liver disease may not cause any symptoms at earlier stage or the symptoms may be vague, like weakness and loss of energy. Symptoms partly depend on the type and the extent of liver disease. Liver diseases are diagnosed based on the liver functional test. Though this disease cannot be predicted at earlier stage due to lack of symptoms and signs, in this paper we attempt to apply soft computing technique for intelligent diagnosis of liver disease. The classification and its type detection are implemented in three phases. In first phase, ANN classification is applied for classifying the liver disease. In second phase rough set rule induction using LEM algorithm is applied to generate classification rules. This rule induction overcomes the drawback of MLP and hence improves the accuracy. in third phase fuzzy rules are applied to identify the types of the liver disease. Using LEM algorithm 6 rules are generated with accuracy of 96% in correct classification. On applying rules generated by LEM, improves the classification accuracy by 6% compared to MLP. The 4 fuzzy rules are framed to indentify the types of liver disease.