Support vector machine ensembles for intelligent diagnosis of valvular heart disease.

In this work, we investigate the use of ensemble learning for improving Support vector machines (SVM) classifier which is one of the important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. Researchers have so far shown that the ensemble methods are quite well in many practical classification problems. However, for valvular heart disease detection, there are almost no studies investigating their feasibilities. Thus, in this study we evaluate the performance of three popular ensemble methods for diagnosing of the valvular heart disorders. To evaluate the performance of investigated ensemble methodology, a comparative study is realized by using a data set containing 215 samples. To achieve a comprehensive comparison, we consider the previous results reported by earlier methods. Experimental results suggest the feasibilities of ensemble of SVM classification methods, and we also derive some valuable conclusions on the performance of ensemble methods for valvular heart disease detection.