Diagnosis is one of the most important processes in the medical field. Since the knowledge domains of clinical specialties are expanding rapidly in terms of complexity and volume of data, clinicians have, in many cases, difficulties to make an accurate diagnosis. Therefore, intelligent and quantitative support for diagnostic tasks can be effectively exploited for improving the effectiveness of the process and reduce misdiagnosis. In this respect, Multi-Classifier Systems represent one of the most promising approaches within Machine Learning methodologies. This paper proposes a Multi-Classifier Systems framework for supporting diagnostic activities with the aim of improving diagnostic accuracy. The framework uses and combines several classification algorithms by dynamically selecting the most competent classifier according to the test sample and its location in the feature space. Here, we extend our previous research. The new experimental results, compared with several multi classifier techniques, based on dynamic classifier selection, on classification datasets, show that the performance of the proposed framework exceeds the state-of-the-art dynamic classifier selection techniques.

Multi-classifier approaches for supporting clinical decision making

Groccia M. C.;Guido R.;Conforti D.
2020

Abstract

Diagnosis is one of the most important processes in the medical field. Since the knowledge domains of clinical specialties are expanding rapidly in terms of complexity and volume of data, clinicians have, in many cases, difficulties to make an accurate diagnosis. Therefore, intelligent and quantitative support for diagnostic tasks can be effectively exploited for improving the effectiveness of the process and reduce misdiagnosis. In this respect, Multi-Classifier Systems represent one of the most promising approaches within Machine Learning methodologies. This paper proposes a Multi-Classifier Systems framework for supporting diagnostic activities with the aim of improving diagnostic accuracy. The framework uses and combines several classification algorithms by dynamically selecting the most competent classifier according to the test sample and its location in the feature space. Here, we extend our previous research. The new experimental results, compared with several multi classifier techniques, based on dynamic classifier selection, on classification datasets, show that the performance of the proposed framework exceeds the state-of-the-art dynamic classifier selection techniques.
Clinical decision support systems
Diagnostic processes
Dynamic classifier selection
Machine learning
Multi-classifier systems
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/307645
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact