Multivariate Prediction Model for Early Detection and Classification of Bacterial Species in Diabetic Foot Ulcers
Authors
Azian Azamimi Abdullah, Nurlisa Yusuf, Mohammad Iqbal Omar, Ammar Zakaria, Latifah Munirah Kmarudin, Ali Yeon Md Shakaff, Abdul Hamid Adom, Maz Jamilah Aznan
Corresponding Author
Azian Azamimi Abdullah
Available Online January 2014.
- 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/).
Cite this article
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 -