A study of creatinine level among patients with dyslipidemia and type 2 diabetes mellitus using multilayer perceptron and multiple linear regression

• Auteurs principaux: Farah Muna Mohamad Ghazali (Auteur), Wan Muhamad Amir W Ahmad (Auteur), Kumar Chandan Srivastava (Auteur), Deepti Shrivastava (Auteur), Nor Farid Mohd Noor (Auteur), Nurul Asyikin Nizam Akbar (Auteur), Nor Azlida Aleng (Auteur), Mohammad Khursheed Alam (Auteur)
• Format: Livre
• Publié: Wolters Kluwer Medknow Publications, 2021-01-01T00:00:00Z.
• Sujets: article
• Accès en ligne: Connect to this object online.
• Notes: 0975-7406; 10.4103/jpbs.JPBS_778_20
• Keywords: dyslipidemia; mean square error; multilayer perceptron neural network; multiple linear regression; type 2 diabetes mellitus; Pharmacy and materia medica; RS1-441; Analytical chemistry; QD71-142
• Source: Journal of Pharmacy and Bioallied Sciences, Vol 13, Iss 5, Pp 795-800 (2021)

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.