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Detection of Resistivity for Antibiotics by Probabilistic Neural Networks

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Abstract

This paper presents the use of probabilistic neural networks (PNNs) for detection of resistivity for antibiotics (resistant and sensitive). The PNN is trained on the resistivity or sensitivity to the antibiotics of each record in the Salmonella database. Estimation of the whole parameter space for the PNN was performed by the maximum-likelihood (ML) estimation method. The expectation-maximization (EM) approach can help to achieve the ML estimation via iterative computation. Resistivity and sensitivity of the three antibiotics (ampicillin, chloramphenicol disks and trimethoprim–sulfamethoxazole) were classified with high accuracies by the PNN. The obtained results demonstrated the success of the PNN to help in detection of resistivity for antibiotics.

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Authors and Affiliations

  1. Departmant of Microbiology, Faculty of Medicine, Kocaeli University, Umuttepe Campus, 41380, Kocaeli, Turkey

    Fatma Budak

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Üniversitesi, 06530, Söğütözü, Ankara, Turkey

    Elif Derya Übeyli

Authors
  1. Fatma Budak
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  2. Elif Derya Übeyli
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Correspondence to Elif Derya Übeyli.

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Budak, F., Übeyli, E.D. Detection of Resistivity for Antibiotics by Probabilistic Neural Networks. J Med Syst 35, 87–91 (2011). https://doi.org/10.1007/s10916-009-9344-z

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  • DOI: https://doi.org/10.1007/s10916-009-9344-z

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