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 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
Pages61-66
Number of pages6
DOIs
Publication statusPublished - 16 Jul 2012
Externally publishedYes
Event9th IASTED International Conference on Biomedical Engineering, BioMed 2012 - Innsbruck, Austria
Duration: 15 Feb 201217 Feb 2012

Publication series

NameProceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012

Conference

Conference9th IASTED International Conference on Biomedical Engineering, BioMed 2012
CountryAustria
CityInnsbruck
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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  • Cite this

    Ng, E., Hambly, B., Matthews, S., McLachlan, C. S., & Jelinek, H. F. (2012). Weka machine learning classification in identifying autonomic dysfunction parameters associated with ACE insertion/deletion genotypes. In Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012 (pp. 61-66). (Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012). https://doi.org/10.2316/P.2012.764-084