Multi-Classifier Approaches for Supporting Clinical Diagnosis

  • Maria Carmela GrocciaEmail author
  • Rosita Guido
  • Domenico Conforti
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 217)


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.


Multi-classifier systems Diagnostic decision support systems Machine learning 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maria Carmela Groccia
    • 1
    Email author
  • Rosita Guido
    • 1
  • Domenico Conforti
    • 1
  1. Lab, Department of Mechanical, Energy and Management EngineeringUniversity of CalabriaRendeItaly

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