Classification of medical data with a robust multi-level combination scheme

Computer aided diagnosis is based on classification of medical data by intelligent classifiers. Especially for medical purposes, the classification must be very efficient, as diagnosis demands a high rate of reliability. Under most circumstances, single classifiers, such as neural networks, support vector machines and decision trees, exhibit worse performance than ensemble combinations of them such as bagging and boosting. In order to further enhance performance, we propose here a combination of these combination methods in a multi-level combination scheme. After experimentation by using four medical diagnosis problems, the proposed approach seems to be efficient in decreasing the error, compared to the best combining method standalone.