Weka machine learning classification in identifying autonomic dysfunction parameters associated with ACE insertion/deletion genotypes

Ethan Ng, Brett Hambly, Slade Matthews, Craig S. McLachlan, Herbert F. Jelinek

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

    3 Citations (Scopus)

    Abstract

    This study was designed to investigate parameters of autonomic dysfunction that may be under the influence of ACE ID genotypes. 136 patients with (47) and without type II diabetes were genotyped. Biomarkers such as HbAlc and eGFR, blood pressure, blood cholesterol are in part regulated by the autonomic nervous system and heart rate variability is an indicator of autonomic balance between the sympathetic and parasympathetic division. Several statistical methods were used, including the J48 decision tree machine learning algorithm to associate parameters of autonomic dysfunction and other biomarkers with ACE genotype. Non-parametric and machine learning methods detected more variables, which were able to contribute to classification of patients into genotypes. We found that HbAlc and TC:HDL were important nodes for separation of ACE genotype classes when the J48 decision tree algorithm was used. These were also verified by the Mann-Whitney analysis. Parametric comparisons of normally distributed variables revealed that only HDL was significantly different between the genotypes. Our findings potentially demonstrate an association between parameters of autonomic dysfunction with ACE genotypes.
    Original languageEnglish
    Title of host publicationProceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012
    EditorsC. Hellmich, M. H. Hamza, D. Simsik
    Place of PublicationCanada
    PublisherACTA Press
    Pages61-66
    Number of pages6
    ISBN (Print)9780889869097
    DOIs
    Publication statusPublished - 2012
    EventIASTED International Conference on Biomedical Engineering (9th : 2012) - Innsbruck, Austria
    Duration: 15 Feb 201217 Feb 2012

    Conference

    ConferenceIASTED International Conference on Biomedical Engineering (9th : 2012)
    CityInnsbruck, Austria
    Period15/02/1217/02/12

    Keywords

    • ACE I/D polymorphism
    • Autonomic dysfunction
    • Classification
    • Genotypes
    • Heart rate variability
    • Machine learning algorithms
    • Renin angiotensin system

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