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

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 asHbA1c 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 HbA1c 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.