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Junbeom Kim(김준범),KyoJoong Oh(오교중),Keun-Whee Oh(오근휘),Ho-Jin Choi(최호진) 한국정보과학회 2011 한국정보과학회 학술발표논문집 Vol.38 No.1C
This research deals with an issue of preventive medicine in bioinformatics. We can diagnose liver conditions reasonably well to prevent Liver Cirrhosis by classifying liver disorder patients into fatty liver and high risk groups. The classification proceeds in two steps. Classification rules are first built by clustering five attributes (MCV, ALP, ALT, ASP, and GGT) of blood test dataset provided by the UCI Repository. The clusters can be formed by the K-mean method that analyzes multi dimensional attributes. We analyze the properties of each cluster divided into fatty liver, high risk and normal classes. The classification rules are generated by the analysis. In this paper, we suggest a method to diagnosis and predict liver condition to alcoholic patient according to risk levels using the classification rule from the new results of blood test. The K-mean classifier has been found to be more accurate for the result of blood test and provides the risk of fatty liver to normal liver conditions.