Creatinine, Urea and Uric Acid in Hospitalized Patients with and Without Hyperglycemia Analysis using Generalized Additive Model

Journal Title: Transactions on Machine Learning and Artificial Intelligence - Year 2017, Vol 5, Issue 4

Abstract

Hyperglycemia is an important risk factor for heart disease and premature 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 analyzi the difference between groups of patients which are separated according 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 set recorded 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 for males. The model which explain the eGFR shows a nonlinear relation between dependent variable eGFR and some predictors (e.g. urea, calcium and uric acid parameters).

Authors and Affiliations

Souad Bechrouri, Abdelilah Monir, Hamid Mraoui, Mohamed Choukri, Ennouamane Saalaoui

Keywords

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  • EP ID EP309171
  • DOI 10.14738/tmlai.54.3212
  • Views 67
  • Downloads 0

How To Cite

Souad Bechrouri, Abdelilah Monir, Hamid Mraoui, Mohamed Choukri, Ennouamane Saalaoui (2017). Creatinine, Urea and Uric Acid in Hospitalized Patients with and Without Hyperglycemia Analysis using Generalized Additive Model. Transactions on Machine Learning and Artificial Intelligence, 5(4), 403-410. https://europub.co.uk/articles/-A-309171