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A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression

BACKGROUND AND 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...

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Autores principales: Ghazali, Farah Muna Mohamad, W Ahmad, Wan Muhamad Amir, Srivastava, Kumar Chandan, Shrivastava, Deepti, Noor, Nor Farid Mohd, Akbar, Nurul Asyikin Nizam, Aleng, Nor Azlida, Alam, Mohammad Khursheed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375798/
https://www.ncbi.nlm.nih.gov/pubmed/34447203
http://dx.doi.org/10.4103/jpbs.JPBS_778_20
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author Ghazali, Farah Muna Mohamad
W Ahmad, Wan Muhamad Amir
Srivastava, Kumar Chandan
Shrivastava, Deepti
Noor, Nor Farid Mohd
Akbar, Nurul Asyikin Nizam
Aleng, Nor Azlida
Alam, Mohammad Khursheed
author_facet Ghazali, Farah Muna Mohamad
W Ahmad, Wan Muhamad Amir
Srivastava, Kumar Chandan
Shrivastava, Deepti
Noor, Nor Farid Mohd
Akbar, Nurul Asyikin Nizam
Aleng, Nor Azlida
Alam, Mohammad Khursheed
author_sort Ghazali, Farah Muna Mohamad
collection PubMed
description BACKGROUND AND 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: 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: 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: 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.
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spelling pubmed-83757982021-08-25 A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression Ghazali, Farah Muna Mohamad W Ahmad, Wan Muhamad Amir Srivastava, Kumar Chandan Shrivastava, Deepti Noor, Nor Farid Mohd Akbar, Nurul Asyikin Nizam Aleng, Nor Azlida Alam, Mohammad Khursheed J Pharm Bioallied Sci Original Article BACKGROUND AND 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: 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: 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: 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. Wolters Kluwer - Medknow 2021-06 2021-06-05 /pmc/articles/PMC8375798/ /pubmed/34447203 http://dx.doi.org/10.4103/jpbs.JPBS_778_20 Text en Copyright: © 2021 Journal of Pharmacy and Bioallied Sciences https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Ghazali, Farah Muna Mohamad
W Ahmad, Wan Muhamad Amir
Srivastava, Kumar Chandan
Shrivastava, Deepti
Noor, Nor Farid Mohd
Akbar, Nurul Asyikin Nizam
Aleng, Nor Azlida
Alam, Mohammad Khursheed
A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression
title A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression
title_full A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression
title_fullStr A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression
title_full_unstemmed A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression
title_short A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression
title_sort study of creatinine level among patients with dyslipidemia and type 2 diabetes mellitus using multilayer perceptron and multiple linear regression
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375798/
https://www.ncbi.nlm.nih.gov/pubmed/34447203
http://dx.doi.org/10.4103/jpbs.JPBS_778_20
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