Using ensemble classification methods in lung cancer disease

This paper presents an overview of the use of ensemble classification methods in the lung cancer disease. An analysis is carried out according to seven aspects: publication trends, channels and venues; medical tasks tackled; 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. The application of ensemble methods in lung cancer disease started in 2003. The diagnosis task was the most tackled one by researchers. Furthermore, the homogeneous ensembles were the most frequent in the literature, and decision tree techniques were the most adopted ones for constructing ensembles. Several datasets related to the lung cancer disease were used to build and assess the ensemble methods. The most used tool was Weka. To conclude, some recommendations for future research are: tackle the medical tasks not investigated in the literature by means of ensemble methods; investigate other classification methods; propose other heterogeneous ensemble methods; and use other combination rules.