SUPERVISED IDENTIFICATION OF ACINETOBACTER BAUMANNI STRAINS USING ARTIFICIAL NEURAL NETWORK

  • Mohd Izzuddin Mohd Tamrin Kulliyyah of ICT, International Islamic University Malaysia, Gombak, Malaysia
  • Mohd Hafidz Mahamad Maifiah International Institute of Halal Research and Training, Gombak, Malaysia
  • Mohd Zulfaezal Che Azemin Kulliyyah of Allied Health, International Islamic University Malaysia, Gombak, Malaysia
  • Sherzod Turaev Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Abu Dhabi, United Arab Emirates
  • Mohamed Jalaldeen Mohamed Razi Faculty of Commerce and Management Studies, University of Kelaniya, Kelaniya, Sri Lanka
Keywords: Acinetobacter Baumannii, Artificial Neural Network (ANN)

Abstract

In hospital environments around the world bacterial contamination is prevalance. 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 resistance to antibiotics. Thus, identification of the non-resistance from the resistance bacteria strain is of important for the correct course of treatments. We proposed to use the artificial neural network (ANN) for supervised identification of this bacteria. The mass spectra generated from the liquid chromatography mass spectrometry (LCMS) will be 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 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.

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Published
2019-12-01
How to Cite
Mohd Tamrin, M. I., Mahamad Maifiah, M. H., Che Azemin, M. Z., Turaev, S., & Mohamed Razi, M. J. (2019). SUPERVISED IDENTIFICATION OF ACINETOBACTER BAUMANNI STRAINS USING ARTIFICIAL NEURAL NETWORK. Journal of Information Systems and Digital Technologies, 1(2), 16-23. Retrieved from https://journals.iium.edu.my/kict/index.php/jisdt/article/view/105