Homogenous Ensembles of Data Mining Algorithms in Predicting Liver Disease

摘要

Application of data mining algorithms to medical fields have been of interest as it helps patients get access to a better and faster healthcare. In this study, the effect of homogenous ensemble methods of bagging and boosting has been investigated as related to the prediction of the presence or absence of liver diseases. Experimental results show that while bagging and boosting did not improve the accuracy and sensitivity of algorithms in predicting liver disease, Boosting increased the specificity of algorithms.