An efficient multi-classifier method for differential diagnosis

Abstract

There are many useful data mining methods for diagnosis of diseases and cancers. However, early diagnosis of a disease or cancer could significantly affect the chance of patient survival in some cases. The objective of this study is to develop a method for helping accurate diagnosis of different diseases based on various classification methods. Knowledge collection from domain experts is challenging, inaccessible and time-consuming; so we design a multi-classifier using a dynamic classifier and clustering selection approach to takes advantages of these methods based on data. We combine Forward-backward and Principal Component Analysis for feature reduction. The multi-classifier evaluates three clustering methods and ascertains the best classification methods in each cluster based on some training data. In this study, we use ten datasets taken from Machine Learning Repository datasets of the University of California at Irvine (UCI). The proposed multi-classifier improves both computation time and accuracy as compared with all other classification methods. It achieves maximum accuracy with minimum standard deviation over the sampled datasets.