skip to main content
10.1145/3375923.3375956acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicbbeConference Proceedingsconference-collections
research-article

Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN

Published: 25 March 2020 Publication History

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.

References

[1]
D. Jasovský, J. Littmann, A. Zorzet, and O. Cars.2016. "Antimicrobial resistance-a threat to theworld's sustainable development". Upsala journal of medical sciences, 121(3), p.159--164.
[2]
S. I. C. J. Palma, A. P. Traguedo, A. R. Porteira, M. J. Frias, H. Gamboa, and A. C. A. Roque. 2018. "Machine learning for the meta-analyses of microbial pathogens' volatile signatures". Scientific Reports, 8(1), p. 3360.
[3]
S. Sethi, R. Nanda, and T. Chakraborty. 2013. "Clinical Application of Volatile Organic Compound Analysis for Detecting Infectious Diseases". Clinical Microbiology Reviews, 26(3), p. 462--475.
[4]
M. Akhil jabbara, B.L.Deekshatulu and Priti Chandra. 2013. "Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm". International Conference on Computational Intelligence: Modeling Techniques and Applications( CIMTA).
[5]
V. B. S. Prasath, H. A. A. Alfeilat, O. Lasassmeh, and A. B. A. Hassanat. 2107. "Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier - A Review". ArXiv170804321 Cs.
[6]
L. D. J. Bos, P. J. Sterk, and M. J. Schultz. 2013. "Volatile Metabolites of Pathogens: A Systematic Review". PLoS Pathogens, 9(5).
[7]
Azian Azamimi Abdullah, Md Altaf-Ul-Amin, Naoaki Ono, Tetsuo Sato, Tadao Sugiura, Aki Hirai Morita, Tetsuo Katsuragi, Ai Muto, Takaaki Nishioka, and Shigehiko Kanaya. 2015. "Development and Mining of a Volatile Organic Compound Database". BioMed Research International, vol. 2015, p. 13, Article ID 139254.
[8]
E. Vigneau, P. Courcoux, R. Symoneaux, L. Guérin, and A. Villiére. 2018. "Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception". Food Quality and Preference, vol. 68, p. 135--145.
[9]
Yu-Hsuan Liao, Zhong-Chuang Wang, Fu-Gui Zhang, Maysam F.Abbod, Chung- Hung Shih and Jiann-Shing Shieh. 2019."Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit.". Sensors, 19(8), pii: E1866.
[10]
K. Chomboon, P. Chujai, P. Teerarassammee, K. Kerdprasop, and N. Kerdprasop. 2015. "An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm". p. 280--285.
[11]
Hu LY, Huang MW, Ke SW and Tsai CF.2016. "The distance function effect on k-nearest neighbor classification for medical datasets". Springerplus, 5(1), p. 1304.
[12]
M. A. Wajeed and T. Adilakshmi. 2011. "Different similarity measures for text classification using kNN".2nd International Conference on Computer and Communication Technology (ICCCT-2011), p. 41--45.
[13]
A. A. Akila and E. Chandra. 2013. "Slope Finder -- A Distance Measure for DTW based Isolated Word Speech Recognition". International Journal Of Engineering And Computer Science, 2(12), p. 3411-3417.
[14]
Ming Zhao and Jingchao Chen. 2016. "Improvement and Comparison of Weighted k Nearest Neighbors Classifiers for Model Selection". Journal of Software Engineering, 10 (1), p.109-118.
[15]
R.Raja Kumar, P.Viswanath and C.Shobha Bindu. 2017. "Nearest Neighbor Classifiers: A Review". International Journal of Computational Intelligence Research, 13(2) ISSN 0973-1873, p. 303-311.
[16]
I. Guyon and A. Elisseeff. 2003. "An Introduction to Variable and Feature Selection". Journal of Machine Learning Research, 3, ISSN 1157-1182 p. 26.
[17]
B. Gholami, I. Norton, A. R. Tannenbaum, and N. Y. R. Agar. 2012. "Recursive Feature Elimination for Brain Tumor Classification using Desorption Electrospray Ionization Mass Spectrometry Imaging",. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5258--5261.
[18]
P. R. Anukrishna and V. Paul.2017. "A review on feature selection for high dimensional data". International Conference on Inventive Systems and Control (ICISC), p. 1--4.

Cited By

View all
  • (2022)Rectified Classifier Chains for Prediction of Antibiotic Resistance From Multi-Labelled Data With Missing LabelsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2022.314857720:1(625-636)Online publication date: 7-Feb-2022

Index Terms

  1. Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICBBE '19: Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering
    November 2019
    214 pages
    ISBN:9781450372992
    DOI:10.1145/3375923
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    In-Cooperation

    • East China Normal University

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 March 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Distance metrics
    2. backward feature elimination (BFE)
    3. bioinformatics
    4. feature selection
    5. machine learning
    6. pathogen detection

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    Conference

    ICBBE '19

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Rectified Classifier Chains for Prediction of Antibiotic Resistance From Multi-Labelled Data With Missing LabelsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2022.314857720:1(625-636)Online publication date: 7-Feb-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media