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

摘要

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.