A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression.
Dyslipidemia
mean square error
multilayer perceptron neural network
multiple linear regression
type 2 diabetes mellitus
Journal
Journal of pharmacy & bioallied sciences
ISSN: 0976-4879
Titre abrégé: J Pharm Bioallied Sci
Pays: India
ID NLM: 101537209
Informations de publication
Date de publication:
Jun 2021
Jun 2021
Historique:
received:
26
11
2020
revised:
08
12
2020
accepted:
10
12
2020
entrez:
27
8
2021
pubmed:
28
8
2021
medline:
28
8
2021
Statut:
ppublish
Résumé
Dyslipidemia is one of the most important risk factors for coronary heart disease with diabetes mellitus. Diabetic dyslipidemia is correlated with reduced concentrations of high-density lipoprotein cholesterol, elevated concentrations of plasma triglycerides, and increased concentrations of dense small particles of low-density lipoprotein cholesterol. Furthermore, dyslipidemia is one of the factors that accelerate renal failure in patients with nephropathy that is observed to be higher in these patients. This paper aims to propose the variable selection using the multilayer perceptron (MLP) neural network methodology before performing the multiple linear regression (MLR) modeling. Dataset consists of patient with Dyslipidemia, and Type 2 Diabetes Mellitus was selected to illustrate the design-build methodology. According to clinical expert's opinion and based on their assessment, these variables were chosen, which comprises the level of creatinine, urea, total cholesterol, uric acid, sodium, and HbA1c. At the first stage, all the selected variables will be a screen for their clinical important point of view, and it was found that creatinine has a significant relationship to the level of urea reading, a total of cholesterol reading, and the level of uric acid reading. By considering the level of significance, α = 0.05, these three variables are being selected and used for the input of the MLP model. Then, the MLR is being applied according to the best variable obtained through MLP process. Through the testing/out-sample mean squared error (MSE), the performance of MLP was assessed. MSE is an indication of the distance from the actual findings from our estimates. The smallest MSE of the MLP shows the best variable selection combination in the model. In this research paper, we also provide the R syntax for MLP better illustration. The key factors associated with creatinine were urea, total cholesterol, and uric acid in patients with dyslipidemia and type 2 diabetes mellitus.
Sections du résumé
BACKGROUND AND OBJECTIVE
OBJECTIVE
Dyslipidemia is one of the most important risk factors for coronary heart disease with diabetes mellitus. Diabetic dyslipidemia is correlated with reduced concentrations of high-density lipoprotein cholesterol, elevated concentrations of plasma triglycerides, and increased concentrations of dense small particles of low-density lipoprotein cholesterol. Furthermore, dyslipidemia is one of the factors that accelerate renal failure in patients with nephropathy that is observed to be higher in these patients. This paper aims to propose the variable selection using the multilayer perceptron (MLP) neural network methodology before performing the multiple linear regression (MLR) modeling. Dataset consists of patient with Dyslipidemia, and Type 2 Diabetes Mellitus was selected to illustrate the design-build methodology. According to clinical expert's opinion and based on their assessment, these variables were chosen, which comprises the level of creatinine, urea, total cholesterol, uric acid, sodium, and HbA1c.
MATERIALS AND METHODS
METHODS
At the first stage, all the selected variables will be a screen for their clinical important point of view, and it was found that creatinine has a significant relationship to the level of urea reading, a total of cholesterol reading, and the level of uric acid reading. By considering the level of significance, α = 0.05, these three variables are being selected and used for the input of the MLP model. Then, the MLR is being applied according to the best variable obtained through MLP process.
RESULTS
RESULTS
Through the testing/out-sample mean squared error (MSE), the performance of MLP was assessed. MSE is an indication of the distance from the actual findings from our estimates. The smallest MSE of the MLP shows the best variable selection combination in the model.
CONCLUSION
CONCLUSIONS
In this research paper, we also provide the R syntax for MLP better illustration. The key factors associated with creatinine were urea, total cholesterol, and uric acid in patients with dyslipidemia and type 2 diabetes mellitus.
Identifiants
pubmed: 34447203
doi: 10.4103/jpbs.JPBS_778_20
pii: JPBS-13-795
pmc: PMC8375798
doi:
Types de publication
Journal Article
Langues
eng
Pagination
S795-S800Informations de copyright
Copyright: © 2021 Journal of Pharmacy and Bioallied Sciences.
Déclaration de conflit d'intérêts
There are no conflicts of interest.
Références
Arch Intern Med. 2004 Jul 12;164(13):1422-6
pubmed: 15249351
Drugs. 1999;58 Suppl 1:47-51; discussion 75-82
pubmed: 10576525
Diabetes Metab J. 2017 Apr;41(2):128-134
pubmed: 28447439
BMJ. 1998 Mar 14;316(7134):823-8
pubmed: 9549452
Med Arch. 2019 Jun;73(3):154-156
pubmed: 31404126
Nat Clin Pract Endocrinol Metab. 2009 Mar;5(3):150-9
pubmed: 19229235
J Clin Diagn Res. 2017 Jan;11(1):BC01-BC04
pubmed: 28273960
Endocrinol Metab Clin North Am. 2006 Sep;35(3):491-510, vii-viii
pubmed: 16959582
J Diabetes Investig. 2019 Sep;10(5):1209-1214
pubmed: 30756513
Int J Environ Res Public Health. 2020 Jul 21;17(14):
pubmed: 32708165
Curr Diab Rep. 2008 Feb;8(1):71-7
pubmed: 18367002