Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review.

Abstract

This paper explores the use of ensemble classification methods in the context of the diabetes disease. An analysis was carried out that formulates and answers seven research questions: publication trends, channels and venues; medical tasks undertaken; ensemble types proposed; single techniques used to construct the ensemble methods; rules used to draw the output of the ensemble; datasets used to build and evaluate the ensemble methods; and tools used. A total of 107 papers were chosen after a study selection process. Ensemble methods were applied to diabetes in 2003 for the first time. All medical tasks related to the diabetes disease were investigated, and the diagnosis task was the most frequently addressed activity by means of ensemble methods. The homogeneous ensembles were the most common in the literature. Moreover, decision trees and support vector machines were the most used techniques to build homogeneous and heterogeneous ensembles, respectively. The most frequently found combiner was the majority voting rule. Our findings suggest that ensemble classification methods yield better accuracy than single classifiers. This statement, however, requires an aggregation of the evidence reported in the literature by means of a systematic literature review.