Abstract

Cardiovascular disease (CVD) is considered as the leading cause of morbidity and mortality in type 2 diabetes (T2D) patients. In 2008 the US FDA issued a Guidance to Industry statement, recognizing the conjoined nature of CVD and T2D and emphasizing the need to monitor cardiovascular risk during new diabetic drug trials. This led researchers to work towards identifying panels of markers that are able to distinguish subtypes of CVD in the context of T2D. Immunoassays are used to detect and quantify biomolecules in a solution. Mass spectrometric immunoassay analysis of various proteins in the blood serum of 212 subjects belonging to multiple disease groups resulted in the identification of 41 molecular species as potential biomarkers. In this paper, support vector machines are used to measure the effectiveness of using these species as a diagnosis tool. We suggest an any-vs-rest SVM multiclass classification method by dividing the problem into a series of binary SVM classification problems and using a MAP decision rule to predict the correct class. One-vs-rest and discriminant analysis approaches are also evaluated for comparison.

Original languageEnglish (US)
Title of host publicationProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Pages338-341
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States
Duration: Dec 18 2011Dec 21 2011

Publication series

NameProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Volume2

Other

Other10th International Conference on Machine Learning and Applications, ICMLA 2011
CountryUnited States
CityHonolulu, HI
Period12/18/1112/21/11

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Buddi, S., Taylor, T., Borges, C., & Nelson, R. (2011). SVM multi-classification of T2D/CVD patients using biomarker features. In Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 (pp. 338-341). [6147700] (Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011; Vol. 2). https://doi.org/10.1109/ICMLA.2011.182