Keywords
diabetes
biomodels
artificial neural network
análisis multidimensional de datos
diabetes
biomodelos
redes neuronales artificiales
How to Cite
Abstract
Biomodels used in the study of diabetes allow to evaluate genetic and environmental factors. Our aim was to characterize individuals of eSS, a genetically diabetic line of rats. We applied multivariate analysis, using the values obtained during the performance of oral glucose tolerance tests, presence of glucosuria, together with other physiological and environmental characteristics totalling 9 variables. Previously, an assignation of missing values of glucosuria was carried out through an artificial neural network classifier. To characterize individuals, principal componentes analysis was carried out. On describing data structure in a graphical representation of factorial coordinates, the first axe separated individuals according to glycemias, age and weight and the second opposed biomass in early ages to litter size. The cluster analysis defined a typology based on five classes. When these results were correlated with clinical classification, it was possible to separate eSS males from the youngest rats with low body weight, aglucosuric, with normal fasting glycemia but impaired glucose tolerance, up to diabetic individuals, older, with higher biomass and glucosuric. This methodology allows to identify stages in the progression of the diabetic syndrome
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