Diabetes mellitus type 2: Exploratory data analysis based on clinical reading

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.date.accessioned2021-06-16T08:43:15Z
dc.date.available2021-06-16T08:43:15Z
dc.date.issued2020-08-11
dc.date.updated2021-06-16T08:43:15Z
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.format.mimetypeapplication/pdf
dc.identifier.idgrec706720
dc.identifier.issn2391-5420
dc.identifier.urihttps://hdl.handle.net/2445/178453
dc.language.isoeng
dc.publisherDe Gruyter Open
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1515/chem-2020-0086
dc.relation.ispartofOpen Chemistry, 2020, vol. 18, p. 1041-1053
dc.relation.urihttps://doi.org/10.1515/chem-2020-0086
dc.rightscc-by (c) Nedyalkova, Miroslava et al., 2020
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceArticles publicats en revistes (Ciència dels Materials i Química Física)
dc.subject.classificationDiabetis no-insulinodependent
dc.subject.classificationTrastorns del metabolisme
dc.subject.classificationQuimioteràpia
dc.subject.otherNon-insulin-dependent diabetes
dc.subject.otherDisorders of metabolism
dc.subject.otherChemotherapy
dc.titleDiabetes mellitus type 2: Exploratory data analysis based on clinical reading
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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