Ensemble classifier systems are considered as one of the most promising in medical data
classification and the performance of deceision tree classifier can be increased by the
ensemble method as it is proven to be better than single classifiers. However, in a ensemble
settings the performance depends on the selection of suitable base classifier. This research
employed two prominent esemble s namely Adaboost and Bagging with base classifiers
such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) …