Assessment of Hypertensive Patients' Complex Metabolic Status Using Data Mining Methods.

Kovács, Beáta; Németh, Ákos; Daróczy, Bálint; Karányi, Zsolt; Maroda, László; Diószegi, Ágnes; Harangi, Mariann; Páll, Dénes

Kovács, Beáta; Németh, Ákos; Daróczy, Bálint; Karányi, Zsolt; Maroda, László; Diószegi, Ágnes; Harangi, Mariann; Páll, Dénes.

Afiliação

Kovács B; Division of Metabolic Diseases, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
Németh Á; Division of Metabolic Diseases, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
Daróczy B; Institute for Computer Science and Control (SZTAKI), Hungarian Research Network, H-1111 Budapest, Hungary.
Karányi Z; Department of Mathematical Engineering (INMA/ICTEAM), Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium.
Maroda L; Division of Metabolic Diseases, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
Diószegi Á; Department of Medical Clinical Pharmacology, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
Harangi M; Division of Metabolic Diseases, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
Páll D; Division of Metabolic Diseases, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.

J Cardiovasc Dev Dis ; 10(8)2023 Aug 13.

Article em En | MEDLINE | ID: mdl-37623358

Palavras-chave

cardiometabolic; data mining; dyslipidaemia; hypertension; hyperuricemia; metabolic parameters; obesity; prevalence; statin

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: J Cardiovasc Dev Dis Ano de publicação: 2023 Tipo de documento: Article

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