Title
A Kernel Support Vector Machine Based Technique for Crohn's Disease Classification in Human Patients.
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 Policlinico 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.
Year
DOI
Venue
2017
10.1007/978-3-319-61566-0_25
COMPLEX, INTELLIGENT, AND SOFTWARE INTENSIVE SYSTEMS, CISIS-2017
Keywords
Field
DocType
Kernel support vector machine,K-Fold cross-validation,Crohn's disease classification
Kernel (linear algebra),Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Classifier (linguistics)
Conference
Volume
ISSN
Citations 
611
2194-5357
0
PageRank 
References 
Authors
0.34
6
8