Creatinine, Urea and Uric Acid in Hospitalized Patients with and without Hyperglycemia Analysis Using Generalized Additive Model

  • Souad Bechrouri Department of Informatics Mohamed First University Oujda, Morocco
  • Abdelilah Monir Department of Informatics Mohamed First University Oujda, Morocco
  • Hamid Mraoui Department of Informatics Mohamed First University Oujda, Morocco
  • Mohamed Choukri Department of Chemistry Mohamed First University Oujda, Morocco
  • Ennouamane Saalaoui Department of Chemistry Mohamed First University Oujda, Morocco
Keywords: component, hyperglycemia, eGFR, generalized additive model,

Abstract

Hyperglycemia is an important risk factor for heart disease andpremature mortality. In hospitalized patients, it is related to an increase in morbidity and development of other disease like kidney disease. To evaluate the existent relation between hyperglycemia and different biochemical parameters, we have proceeded to analyzing the difference between groups of patients which are separatedaccording to the Glucose critical value (1.26 g/L). Generalized additive models (GAM) was used in the aim to model the relation between estimated glomerular filtration renal (eGFR) and some biochemical parameters.Our study was conducted on a data setrecorded on 5600 hospitalized patients in CHU Oujda. Our statistical study revealed that the hyperglycemic patients present an increase in values of each of uric acid, creatinine, urea and triglycerides. This increase is accompanied by a loss in HDL cholesterol and eGFR.Regarding the gender of patients, results show a difference between males and females according to each of parameters: creatinine, urea, uric acid, total cholesterol, HDL and LDL cholesterol. Moreover, results show lower values of eGFR males.The model which explain the eGFRshows a non-linear relation between dependent variable eGFR and some predictors (e.g. urea, calcium and uric acid parameters).

 

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Published
2017-09-01
Section
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems