Author(s): Preethi Jayarama Shetty and Satyanarayana

Abstract: The liver is the largest solid organ that plays an important role in many bodily functions from protein production and blood clotting to cholesterol. It additionally serves to eliminate harmful waste products and certain drugs, detoxify alcohol, and environmental toxins. The liver forms and secretes digestive fluid that contains digestive fluid acids to help with the digestion, internal organ absorption of fats, and fat-soluble vitamins A, D, E, and K. Diseases that may affect the liver include hepatitis, cirrhosis, fatty liver, and liver cancer. The processing of unbalanced data presents significant difficulties for the researchers in class label identification. In health research, accurate disease diagnosis employing a good classification model would alleviate the strain on doctors and help to prevent major losses. The main focus of the study is to identify the major risk factors associated with liver disease and identifying the best classification model to handle imbalanced liver disease data. The prediction performance of different classification models using SMOTE and ROSS algorithm are compared based on accuracy measures and the best classification model to deal with imbalanced data is reported.

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