Multivariate Prediction Model for Early Detection and Classification of Bacterial Species in Diabetic Foot Ulcers

Keywords

Diabetic, Foot Ulcer, E-Nose, PEN3, Cyranose320, LDA, KNN, PNN, SVM, RBF.

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 bacte-ria 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, effi-ciency, reliability and reduced diagnosis time in identifying bacterial species on foot ulcer infection.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Azian Azamimi Abdullah
AU  - Nurlisa Yusuf
AU  - Mohammad Iqbal Omar
AU  - Ammar Zakaria
AU  - Latifah Munirah Kmarudin
AU  - Ali Yeon Md Shakaff
AU  - Abdul Hamid Adom
AU  - Maz Jamilah Aznan
PY  - 2014/01
DA  - 2014/01
TI  - Multivariate Prediction Model for Early Detection and Classification of Bacterial Species in Diabetic Foot Ulcers
BT  - Proceedings of the 2013 International Conference on Advances in Intelligent Systems in Bioinformatics
PB  - Atlantis Press
SP  - 32
EP  - 39
SN  - 1951-6851
UR  - https://www.atlantis-press.com/article/11354
ID  - Abdullah2014/01
ER  -