Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6520
Title: Detection of Resistivity for Antibiotics by Probabilistic Neural Networks
Authors: Budak, Fatma
Übeyli, Elif Derya
Keywords: Probabilistic neural network
Expectation-Maximization algorithm
Resistivity to antibiotics
Sensitivity to antibiotics
Classification accuracy
Issue Date: 2011
Publisher: Springer
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.
URI: https://doi.org/10.1007/s10916-009-9344-z
https://hdl.handle.net/20.500.11851/6520
ISSN: 0148-5598
1573-689X
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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