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Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%-30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia. Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping. Results: Variable validation was performed by using the well-established bootstrap-integrated MLR technique. All variables affected the hazard ratio as follows: total cholesterol (ß1: -0.00664; p < 0.25), diabetes status (ß2: 0.62332; p < 0.25), diastolic reading (ß3: 0.08160; p < 0.25), height measurement (ß4: -0.05411; p < 0.25), coronary heart disease incidence (ß5: 1.42544; p < 0.25), triglyceride reading (ß6: 0.00616; p < 0.25), and waist reading (ß7: -0.00158; p < 0.25). Conclusions: A hybrid approach was developed and extensively tested. The hybrid technique is superior to other standalone techniques and allows an improved understanding of the influence of variables on outcomes. [ABSTRACT FROM AUTHOR]

Background and Objective: The incidence of type 2 diabetes has been steadily rising over the past few decades, which has contributed significantly to the rise in the prevalence of diabetes (DM). Statistics from the World Health Organization show that more than 422 million adults worldwide had diabetes in 2014, and an ongoing rise in DM prevalence is anticipated. This study aims to create a method that can use to predict and manage diabetes cases in light of the importance of statistical modeling in diabetes. Decision trees and ordinal regression were the two methods used in this study. With some modification and extension, both methods will be harmonized in the R syntax. Materials and Methods: In this paper, we developed a method for analyzing decision trees using R syntax and embedding classification predictions. The classification for prediction with accuracy will indicate a successful classification analysis. This study illustrated the development method using diabetes data consisting of one thousand observations. Before further testing, the clinical relevance and significance of each preselected variable will be assessed. The decision tree will be used to evaluate nine variables. The selected variables are body mass index, total cholesterol, diabetes status, glucose reading, high-density lipoprotein, patient height, hip circumference, hypertension status, smoking status, and triglycerides. The classification obtained will be used as an input for the ordinal regression modeling. Result: It has been discovered that the status of diabetes can be determined by the level of glucose during fasting, which is consistent with the most recent research that has been published. one variable was chosen and used for the input of the ordinal regression. The suggested variables will apply to the ordered logistic regression, and the developed syntax will be used to assess the goodness of measurement and the significance level is set at a 0.05 level. Conclusion: Our proposed method achieves the highest level of forecasting precision possible. The methodology offers a precise evaluation of the fit of the final model. The superior performance of the model resulted in improved outcomes and efficient decision-making management. [ABSTRACT FROM AUTHOR]

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, a = 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. [ABSTRACT FROM AUTHOR]

Background:Hypertension is a public health problem used to describe high blood pressure where the blood vessels are persistently increased in force. According to WHO, hypertension has been reported in one in four men and one in five women. Worldwide, hypertension is a common health problem that affects 20-30% of the adult population and more than 5-8% of pregnancies, and it is frequently curable when detected and treated early enough. Objective: This paper aims to validate the factor associatedwith hypertension status among patients with dyslipidemia and type 2 diabetes mellitus. This could help to improve the prediction of the probability of hypertension among studied patients. Material and Methods: 39 patients were recruited from the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling methodologies were used to evaluate data descriptions of several variables such as hypertension, marital status, smoking status, systolic blood pressure, fasting blood glucose, total cholesterol, high-density lipoprotein, alanine transferase, alkaline phosphatase, and urea reading. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included bootstrap and multiple logistic regression methods. Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It revealed that the hybrid model technique better predicts the outcome when data is partitioned into a training and testing dataset. The variable validation was determined using the well-established bootstrap-integrated MLRtechnique. In this case, eight variables are considered: marital status, systolic blood pressure, fasting blood glucose, total cholesterol, high-density lipoprotein, alanine transferase, alkaline phosphatase, and urea reading. It's important to note that six things affect the hazard ratio: Marital status (ß1: 1.183519; p< 0.25), systolic blood pressure (:-0.144516; p< 0.25), total cholesterol (ß2: 0.9585890; p< 0.25), high-density lipoprotein (:-5.927411; p< 0.25), alkaline phosphatase (:-0.008973; p> 0.25), and urea reading (:0.064169; p< 0.25). There is a 0.003469102 MSE for the linear model in this scenario. Conclusion: In this study, a hybrid approach combining bootstrapping and multiple logistic regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to understand better the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modelingoutperforms R-squared values of 0.9014 and 0.00882 for the Predicted Mean Squared Error, respectively. Thus, the study's conclusion establishes the superiority of the hybrid model technique used in the study. [ABSTRACT FROM AUTHOR]