A kernel support vector machine based technique for Crohn’s disease classification in human patients

Lo Re, G.

Risultato della ricerca: Chapter

3 Citazioni (Scopus)

Abstract

In this paper a new technique for classification of patients affected by Crohn’s disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The effectiveness of the proposed technique has been proved using a real human patient dataset collected at the University of Palermo Policlin-ico Hospital (UPPH dataset) and composed of 300 patients. The KSVM classification results have been compared against the histological specimen results, which are the adopted Ground-Truth for CD diagnosis. The achieved results (Sensitivity: 94,80%; Specificity: 100,00%; Negative Predictive Value: 95,06%; Precision: 100,00%; Accuracy: 97,40%; Error: 2,60%) show that the proposed technique results are comparable or even better than manual reference methods reported in literature.
Lingua originaleEnglish
Titolo della pubblicazione ospiteComplex, Intelligent, and Software Intensive Systems
Pagine262-273
Numero di pagine12
Volume611
Stato di pubblicazionePublished - 2018

Serie di pubblicazioni

NomeADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING

Fingerprint

Support vector machines
Classifiers

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cita questo

Lo Re, G. (2018). A kernel support vector machine based technique for Crohn’s disease classification in human patients. In Complex, Intelligent, and Software Intensive Systems (Vol. 611, pagg. 262-273). (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING).

A kernel support vector machine based technique for Crohn’s disease classification in human patients. / Lo Re, G.

Complex, Intelligent, and Software Intensive Systems. Vol. 611 2018. pag. 262-273 (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING).

Risultato della ricerca: Chapter

Lo Re, G. 2018, A kernel support vector machine based technique for Crohn’s disease classification in human patients. in Complex, Intelligent, and Software Intensive Systems. vol. 611, ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING, pagg. 262-273.
Lo Re, G. A kernel support vector machine based technique for Crohn’s disease classification in human patients. In Complex, Intelligent, and Software Intensive Systems. Vol. 611. 2018. pag. 262-273. (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING).
Lo Re, G. / A kernel support vector machine based technique for Crohn’s disease classification in human patients. Complex, Intelligent, and Software Intensive Systems. Vol. 611 2018. pagg. 262-273 (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING).
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abstract = "In this paper a new technique for classification of patients affected by Crohn{\^a}€™s disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The effectiveness of the proposed technique has been proved using a real human patient dataset collected at the University of Palermo Policlin-ico Hospital (UPPH dataset) and composed of 300 patients. The KSVM classification results have been compared against the histological specimen results, which are the adopted Ground-Truth for CD diagnosis. The achieved results (Sensitivity: 94,80{\%}; Specificity: 100,00{\%}; Negative Predictive Value: 95,06{\%}; Precision: 100,00{\%}; Accuracy: 97,40{\%}; Error: 2,60{\%}) show that the proposed technique results are comparable or even better than manual reference methods reported in literature.",
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T1 - A kernel support vector machine based technique for Crohn’s disease classification in human patients

AU - Lo Re, G.

AU - Petrucci, Giovanni

AU - Salerno, Sergio

AU - Vitabile, Salvatore

AU - Scopelliti, Laura

AU - Comelli, Albert

AU - Terranova, Maria Chiara

AU - Midiri, Federico

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N2 - In this paper a new technique for classification of patients affected by Crohn’s disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The effectiveness of the proposed technique has been proved using a real human patient dataset collected at the University of Palermo Policlin-ico Hospital (UPPH dataset) and composed of 300 patients. The KSVM classification results have been compared against the histological specimen results, which are the adopted Ground-Truth for CD diagnosis. The achieved results (Sensitivity: 94,80%; Specificity: 100,00%; Negative Predictive Value: 95,06%; Precision: 100,00%; Accuracy: 97,40%; Error: 2,60%) show that the proposed technique results are comparable or even better than manual reference methods reported in literature.

AB - In this paper a new technique for classification of patients affected by Crohn’s disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The effectiveness of the proposed technique has been proved using a real human patient dataset collected at the University of Palermo Policlin-ico Hospital (UPPH dataset) and composed of 300 patients. The KSVM classification results have been compared against the histological specimen results, which are the adopted Ground-Truth for CD diagnosis. The achieved results (Sensitivity: 94,80%; Specificity: 100,00%; Negative Predictive Value: 95,06%; Precision: 100,00%; Accuracy: 97,40%; Error: 2,60%) show that the proposed technique results are comparable or even better than manual reference methods reported in literature.

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