MODELING CHOLESTEROL LEVELS IN PATIENTS WITH DYSLIPIDEMIA AND TYPE 2 DIABETES MELLITUS USING AN INTEGRATED STATISTICAL METHOD

Abstract

Background: Cholesterol levels in the blood, comprising both LDL and HDL cholesterol, can lead to artery plaque formation and potential blockages. Researchers are studying cholesterol levels in individuals with dyslipidemia and type 2 diabetes mellitus. Objective: The objective of this paper is to utilize the developed methodology to model the factor associated with the total cholesterol status in patients with dyslipidemia and type 2 diabetes mellitus. This undertaking has the potential to improve the prediction of total cholesterol levels among the analyzed patients by integrating comprehensive supplementary data from a statistical standpoint. Material and Methods: The data was collected from Hospital Universiti Sains Malaysia (Hospital USM), using statistical modelling techniques to evaluate data descriptions of numerous variables, including height, total cholesterol, triglyceride levels, low-density lipoprotein (LDL) levels, high-density lipoprotein (HDL) levels, and alkaline phosphatase (ALP) levels. The developed method was implemented and evaluated using the R-Studio, employing a neural network model with bootstrapping method and response surface methodology. Results: Our proposed method showed superior accuracy when dividing data into training and testing sets, offering a more precise prediction. The neural network's mean square error was approximately 0.021, demonstrating high precision. Conclusion: In this study, the proposed model demonstrates the method's capability for the improvement of research methodology. The outcome suggests that the methodology established for this investigation is capable of producing favourable results. The study's final analysis demonstrates that the model technique created for research is preferable.