Supervised identification of Acinetobacter Baumanni strains using artificial neural network

Mohd Tamrin, Mohd Izzuddin and Mahamad Maifiah, Mohd Hafidz and Che Azemin, Mohd Zulfaezal and Turaev, Sherzod and Mohamed Razi, Mohamed Jalaldeen (2019) Supervised identification of Acinetobacter Baumanni strains using artificial neural network. Journal of Information Systems and Digital Technologies, 1 (2). pp. 16-23. E-ISSN 2682-8790

PDF (Supervised Identification Of Acinetobacter Baumanni Strains Using Artificial Neural Network) - Published Version Restricted to Repository staff only Download (755kB) | Request a copy

Abstract

In hospital environments around the world bacterial contamination is prevalence. One of the most commonly found bacteria is the Acinetobacter Baumannii. It can cause unitary tract, lung, abdominal and central nervous system infection. This bacteria is becoming more resistant to antibiotics. Thus, identification of the non-resistant from the resistant bacteria strain is of important for the correct course of treatments. We propose to use the artificial neural network (ANN) for supervised identification of this bacteria. The mass spectra generated from the liquid chromatography mass spectrometry (LCMS) were used as the features to train the ANN. However, due to the massive number of features, we applied the principle component analysis (PCA) to reduce the dimensions. Less than 1% of the original number of features were utilized. The hand out validation method confirmed that the accuracy, sensitivity and specificity are 0.75 respectively. In order to avoid selection biasness in the sampling, 5-fold cross validation was performed. In comparison, the average accuracy is close to 0.75 but the average sensitivity is slightly higher by 0.50.

Item Type:	Article (Journal)
Additional Information:	5594/76781
Uncontrolled Keywords:	Acinetobacter Baumannii, Artificial Neural Network (ANN).
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	International Institute for Halal Research and Training (INHART) Kulliyyah of Allied Health Sciences Kulliyyah of Allied Health Sciences > Department of Optometry and Visual Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Mohd Izzuddin Mohd Tamrin
Date Deposited:	20 Dec 2019 17:15
Last Modified:	21 Jun 2022 15:41
URI:	http://irep.iium.edu.my/id/eprint/76781

Actions (login required)

View Item