Abstract
Clinical diagnosis processes can result in many cases very complicated. A misdiagnosis is expensive and potentially life-threatening for patients. Diagnosis problems are mainly in the scope of the classification problems. Multi-classifier approaches can improve accuracy in classification task. In this work, we propose Multi-classifier approaches based on dynamic classifier selection techniques. These approaches have been tested on datasets known in the literature and representative of important diagnostic problems. Experimental results show that a suitable pool of different classifiers increases accuracy in classification task. This suggests that the proposed approaches can improve performance of diagnostic decision support systems.
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Groccia, M.C., Guido, R., Conforti, D. (2017). Multi-Classifier Approaches for Supporting Clinical Diagnosis. In: Sforza, A., Sterle, C. (eds) Optimization and Decision Science: Methodologies and Applications. ODS 2017. Springer Proceedings in Mathematics & Statistics, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-67308-0_13
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DOI: https://doi.org/10.1007/978-3-319-67308-0_13
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