On Patient’s Characteristics Extraction for Metabolic Syndrome Diagnosis: Predictive Modelling Based on Machine Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8649)


The work presented in this paper demonstrates how different data mining approaches can be applied to extend conventional combinations of variables determining the Metabolic Syndrome with new influential variables, which are easily available in the everyday physician‘s practice. The results have important consequences: patients with the Metabolic Syndrome can be recognized by using only some, one, or none of the conventional variables, when replaced with some other surrogate variables, available in patient health records, making diagnosis feasible in different work environments and at different time points of patient care. In addition, the results showed that there is a large diversity of patient groups, much larger than it was supposed earlier on when their identification was based on the conventional variables approach, indicating the underlying complexity of this syndrome. Finally, the discovered novel variables, indicating yet unknown pathogenetic pathways can be used to inspire future research.


biomedical data mining metabolic syndrome machine learning 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Informatics, Department of Cybernetics and Artificial IntelligenceTechnical University of KošiceKošiceSlovakia
  2. 2.Josip Juraj Strossmayer UniversityOsijekCroatia
  3. 3.Institute for Medical Informatics, Statistics and Documentation Research Unit HCIMedical University GrazGrazAustria

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