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A mapping study of ensemble classification methods in lung cancer decision support systems

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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 parameter 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.

Main features of the mapping study performed in ensemble classification methods applied on lung cancer decision support systems

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Funding

The work of the first author is partially supported by the European Commission trough the Erasmus+ International Mobility Program (KA107). This work was partly supported by the Spanish MICINN, as well as the European Commission FEDER funds, under grants RTI2018-098156-B-C53 and RTI2018-098309-B-C33.

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Authors and Affiliations

  1. Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

    Mohamed Hosni & Ali Idri

  2. Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Murcia, Spain

    Ginés García-Mateos, Juan M. Carrillo-de-Gea & José Luis Fernández-Alemán

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  1. Mohamed Hosni
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  2. Ginés García-Mateos
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  3. Juan M. Carrillo-de-Gea
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  4. Ali Idri
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Contributions

All authors contributed to the study conception and design. The literature search and data analysis were performed by Mohamed Hosni, Ali Idri, and José Luis Fernández-Alemán. The first draft of the manuscript was written by Mohamed Hosni, Ginés García-Mateos, and Juan M. Carrillo-de-Gea. All authors commented on previous versions of the manuscript, and all authors read and approved the final manuscript.

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Correspondence to Ginés García-Mateos.

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Hosni, M., García-Mateos, G., Carrillo-de-Gea, J.M. et al. A mapping study of ensemble classification methods in lung cancer decision support systems. Med Biol Eng Comput 58, 2177–2193 (2020). https://doi.org/10.1007/s11517-020-02223-8

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