Open AccessA study of creatinine level among patients with dyslipidemia and type 2 diabetes mellitus using multilayer perceptron and multiple linear regression
Author(s) -
Farah Muna Mohamad Ghazali,
Wan Muhamad Amir W Ahmad,
Kumar Chandan Srivastava,
Deepti Shrivastava,
Nor Farid Mohd Noor,
Nurul Akbar,
Nor Azlida Aleng,
Mohammad Khursheed Alam
Publication year - 2021
Publication title -
journal of pharmacy and bioallied sciences
Language(s) - English
Resource type - Journals
eISSN - 0976-4879
pISSN - 0975-7406
DOI - 10.4103/jpbs.jpbs_778_20
Subject(s) - dyslipidemia , creatinine , multilayer perceptron , medicine , uric acid , diabetes mellitus , linear regression , diabetic nephropathy , type 2 diabetes mellitus , mathematics , endocrinology , artificial neural network , statistics , machine learning , computer science
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.