Prediction of Liver Cirrhosis using Ensemble Machine Learning Algorithms

In human body, liver is one of the important organs and is also regarded as a gland because, among other things, it produces and secretes bile. Liver disorders are one of the most common diseases in the world. Any problems with the liver that result in illness are referred to as liver dysfunction diseases. This research work uses different ensemble methods to investigate the detection of liver cirrhosis. The selected dataset for this analysis is made up of many set of diagnosis attributes. Additionally, the primary goal of this research is to evaluate and compare the effectiveness of several ensemble approaches, including the AdaBoost, LogitBoost, and Random Forest algorithms. The prediction results depicts that LogitBoost provides better accuracy as compared with AdaBoost and Random Forest.