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International Conference on Advances in Intelligent Systems in Bioinformatics, 2014, pages 27-34
Multivariate prediction model for early detection and classification
of bacterial species in diabetic foot ulcers
Abstract
Many diabetic patients eventually develop foot ulcers are at risk for further infection and subsequent
amputation if they are not treated promptly. Hence, this study is focused on identifying wild type strain
bacteria and standard ATCC bacteria using e-nose which are PEN3 and Cyranose320. Data collected
from both e-nose are processed using multivariate classifier such as LDA, KNN, PNN, SVM and RBF.
The results indicate that rapid detection of bacteria using e-nose has increased the effectiveness, efficiency, reliability and reduced diagnosis time in identifying bacterial species on foot ulcer infection.
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Keywords — Diabetic, foot ulcer, e-nose, PEN3, Cyranose320, LDA, KNN, PNN, SVM, RBF.
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