Publication: A mapping study of ensemble classification methods in lung cancer decision support systems
Authors
Mohamed Hosni ; García Mateos, Ginés ; Carrillo de Gea, Juan Manuel ; Ali Idri ; Fernández Alemán, José Luis
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Facultad de Informática
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Publisher
Springer Nature
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DOI
https://doi.org/10.1007/s11517-020-02223-8
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info:eu-repo/semantics/article
Description
Abstract
Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameters tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules.
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Citation
Med Biol Eng Comput 58, 2177–2193 (2020)
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