Classification of liver disease diagnosis: a comparative study

• Main Authors: Bahramirad, Sina, Mustapha, Aida, Eshraghi, Maryam
• Format: Conference or Workshop Item
• Published: IEEE (IEEEXplore) 2013
• Online Access: http://psasir.upm.edu.my/id/eprint/41298/

Medical Data Mining (MDM) is one of the most critical aspects of automated disease diagnosis and disease prediction. MDM involves developing data mining algorithms and techniques to analyze medical data. In recent years, liver disorders have excessively increased and liver diseases are becoming one of the most fatal diseases in several countries. In this study, two real liver patient datasets were investigated for building classification models in order to predict liver diagnosis. Eleven data mining classification algorithms were applied to the datasets and the performance of all classifiers are compared against each other in terms of accuracy, precision, and recall. Several investigations have also been carried out to improve performance of the classification models. Finally, the results shown promising methodology in diagnosing liver disease during the earlier stages.