Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/1784
Title: Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN
Authors: Tharmakulasingam, Mukunthan
Topal, Cihan
Fernando, Anil
La Ragione, Roberto
Keywords: Distance metrics
backward feature elimination (BFE)
pathogen detection
bioinformatics
feature selection
machine learning
Issue Date: 2019
Publisher: Assoc Computing Machinery
Abstract: The timely identification of pathogens is vital in order to effectively control diseases and avoid antimicrobial resistance. Non-invasive point-of-care diagnostic tools are recently trending in identification of the pathogens and becoming a helpful tool especially for rural areas. Machine learning approaches have been widely applied on biological markers for predicting diseases and pathogens. However, there are few studies in the literature that have utilized volatile organic compounds (VOCs) as non-invasive biological markers to identify bacterial pathogens. Furthermore, there is no comprehensive study investigating the effect of different distance and similarity metrics for pathogen classification based on VOC data. In this study, we compared various non-Euclidean distance and similarity metrics with Euclidean metric to identify significantly contributing VOCs to predict pathogens. In addition, we also utilized backward feature elimination (BFE) method to accurately select the best set of features. The dataset we utilized for experiments was composed from the publications published between 1977 and 2016, and consisted of associations in between 703 VOCs and 11 pathogens. We performed extensive set of experiments with five different distance metrics in both uniform and weighted manner. Comprehensive experiments showed that it is possible to correctly predict pathogens by using 68 VOCs among 703 with 78.6% accuracy using k-nearest neighbour classifier and Sorensen distance metric.
Description: 6th International Conference on Biomedical and Bioinformatics Engineering (ICBBE) -- NOV 13-15, 2019 -- E China Normal Univ, Shanghai, PEOPLES R CHINA
URI: https://doi.org/10.1145/3375923.3375956
https://hdl.handle.net/20.500.13087/1784
ISBN: 978-1-4503-7299-2
Appears in Collections:Matematik Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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