Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNedyalkova, Miroslava-
dc.contributor.authorMadurga Díez, Sergio-
dc.contributor.authorBallabio, Davide-
dc.contributor.authorRobeva, Ralitsa-
dc.contributor.authorRomanova, Julia-
dc.contributor.authorKichev, Ilia-
dc.contributor.authorElenkova, Atanaska-
dc.contributor.authorSimeonov, Vasil-
dc.description.abstractDiabetes mellitus type 2 (DMT2) is a severe and complex health problem. It is the most common type of diabetes. DMT2 is a chronic metabolic disorder that affects the way your body metabolizes sugar. With DMT2, your body either resists the effects of insulin or does not produce sufficient insulin to continue normal glucose levels. DMT2 is a disease that requires a multifactorial approach of controlling that includes lifestyle change and pharmacotherapy. Less than ideal management increases the risk of developing complications and comorbidities such as cardiovascular disease and numerous social and economic penalties. That is why the studies dedicated to the pathophysiological mechanisms and the treatment of DMT2 are extremely numerous and diverse. In this study, exploratory data analysis approaches are applied for the treatment of clinical and anthropometric readings of patients with DMT2. Since multivariate statistics is a well-known method for classification, modeling and interpretation of large collections of data, the major aim of the present study was to reveal latent relations between the objects of the investigation (group of patients and control group) and the variables describing the objects (clinical and anthropometric parameters). In the proposed method by the application of hierarchical cluster analysis and principal component analysis it is possible to identify reduced number of parameters which appear to be the most significant discriminant parameters to distinguish between four patterns of patients with DMT2. However, there is still lack of multivariate statistical studies using DMT2 data sets to assess different aspects of the problem like optimal rapid monitoring of the patients or specific separation of patients into patterns of similarity related to their health status which could be of help in preparation of data bases for DMT2 patients. The outcome from the study could be of custom for the selection of significant tests for rapid monitoring of patients and more detailed approach to the health status of DMT2 patients.-
dc.format.extent13 p.-
dc.publisherDe Gruyter Open-
dc.relation.isformatofReproducció del document publicat a:
dc.relation.ispartofOpen Chemistry, 2020, vol. 18, p. 1041-1053-
dc.rightscc-by (c) Nedyalkova, Miroslava et al., 2020-
dc.subject.classificationDiabetis no-insulinodependent-
dc.subject.classificationTrastorns del metabolisme-
dc.subject.otherNon-insulin-dependent diabetes-
dc.subject.otherDisorders of metabolism-
dc.titleDiabetes mellitus type 2: Exploratory data analysis based on clinical reading-
Appears in Collections:Articles publicats en revistes (Ciència dels Materials i Química Física)

Files in This Item:
File Description SizeFormat 
706720.pdf769.48 kBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons