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 (β1: 1.183519; p< 0.25), systolic blood pressure ( :-0.144516; p< 0.25), total cholesterol (β2: 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. Bangladesh Journal of Medical Science Vol. 22 No. 02 April’23 Page : 422-431
Objective: The purpose of this study is to find propotion of fractured orbital walls in the maxillofacial trauma cases and its associated maxillofacial fracture treated in the Oral Maxillofacial Clinic Oral Maxillofacial ward and operation theatre of Hospital USM in Kelantan, Malaysia. Materials and methods: From July 2013 to June 2018, records of patients who sustained maxillofacial fractures and presented them to the Accident and Emergency Department, Oral Maxillofacial Clinic, Hospital USM were reviewed, recorded, and analyzed. There are 294 patients whose data has been collected because they met the inclusion criteria. Each patient with a complete medical record was reviewed. Data were collected under the variables: Zygomatic Complex, Zygomatic Arch, Nasal, Maxillary Sinus, Le Fort I, Le Fort II, Le Fort III, Orbital Wall, Alveolar Process, Symphysis of Mandible, Condyle of Mandible, Ramus of Mandible, Maxillary Bone and Mandibular Bone of maxillofacial fracture. The fractured orbital walls in these cases was reviewed. At the first stage, all the selected variables will be screened for their important clinical point of view. The SPSS software version 26.0 was used to determine all possible factors contributing to orbital wall fracture. Results: This was a retrospective cross-sectional analysis of the medical records of 294 patients with maxillofacial fracture treated in the Oral Maxillofacial Clinic and Oral Maxillofacial ward, Hospital USM. There were 228 (77.3%) men and 66 (22.4%) women included in this study. The most common age range is 11-20 years (39.8%), 21-30 years (26.2%). Maxillary Bone Fracture (0.371; p <0.05), Maxillary Sinus Fracture (0.180; p <0.05), Zygomatic Arch Fracture (0.127; p <0.05) were found to be the most affected site, which had a positive correlation with an orbital fracture of the maxillofacial trauma cases. A path analysis based on the Spearman correlation was developed by taking into account significant correlations at the level of 0.05. Conclusion: Using the matrix spearman correlation, multiple response analysis (MRA), path analysis, we discovered a clear connection between orbital wall fracture and several other factors. This discovery will aid in the understanding of the most common fracture and the causes of orbital wall fracture in maxillofacial trauma. The Zygomatic Arch Fracture, Maxillary Sinus Fracture, and Maxillary Bone Fracture were found to have a significant relationship with the orbital wall when the significance level was set at 0.05. Bangladesh Journal of Medical Science Vol. 21 No. 04 October’22 Page : 744-750
Background: The goal of this study is to illustrate an optimum variable selection method using established Multiple Linear Regression (MLR) models and to validate the variable using Multilayer Perceptron Neural Network (MLP) models. Initially, all selected variables will be passed through the bootstrap methodology, and they were screened for significant relationships. Objective: The goal of this work is to analyze and construct a model for the factor linked with total crime cases by combining an Applied Linear Regression Model (ALRM) and a Multilayer Perceptron (MLP). Material and Methods: Around 200 data was simulated to build the methodology. Advanced computational statistical modeling methodologies were used to evaluate data descriptions of several variables in this retrospective study, including the total victim, gender, age, marital status, social class, adult in the household, children in household, burglary’s victim, sexual’s victim, victim’s report, and household location. The case study was developed and implemented using the R-Studio program and syntax. Results: The statistical method demonstrated that regression modeling surpasses R-squared and mean square error test in most situations. Researchers observed that when data is divided into two datasets for training and testing, the hybrid model approach performs significantly better at predicting the experiment’s outcome. When it came time to determine variable validity, the well-established bootstrap-integrated MLR approach was applied. Ten characteristics are taken into consideration in this case: Gender (: -0.4369700; p< 0.25), age (: -0.0086757; p< 0.25), marital status (: 0.2646097; p< 0.25), social class ( : 0.0602540; p< 0.25), adult in household (: -0.0211293; p> 0.25), children in household (: -0.0025346; p> 0.25), burglary’s victim (: 1.3473593; p< 0.25), sexual’s victim (: 1.0382444; p< 0.25), victim’s report (: -0.3176104; p< 0.25), and location of household (: -0.1355046; p< 0.25).There is a 0.07745823 MSE for the linear model in this scenario. Conclusion: The neural network’s Predicted Mean Square Error (PMSE) was used to assess MLP’s performance (MSE-forecasts the Network). PMSE is used to determine how far our projections are from the actual data, and the lowest MSE from the MLP indicates the best achievement. The R syntax for MLR and MLP is also included in this research article.As a result, the study’s conclusion establishes the superiority of the hybrid model technique. Bangladesh Journal of Medical Science Vol. 22 No. 01 January’23 Page : 38-46
Background: Diabetes mellitus is a chronic illness that results in abnormally high blood sugar levels. It can result in a range of complications. Objective: The purpose of this study is to present an ideal variable selection strategy utilizing proven Multiple Linear Regression (MLR) models and to validate the variable using Multilayer Perceptron Neural Network (MLP) models. This will validate a factor linked with body mass index (BMI) status in individuals with dyslipidemia and type 2 diabetes mellitus. Materials and Methods: Thirty-nine patients were selected from Hospital Universiti Sains Malaysia (USM). Many variables, including BMI, gender, age, race, coronary heart disease status, waist circumference, alanine transferase, triglycerides, and dyslipidemia, were assessed in this retrospective analysis using advanced computational statistical modelling approaches. This study uses R-Studio software and syntax. Each sample's statistics were generated using a hybrid model combining bootstrap and multiple linear regression. Results: R's statistical approach demonstrates that regression modelling is superior to R-squared performance. The hybrid model may better predict the outcome by separating the datasets into a training and testing set. The well-known bootstrap-integrated MLR technique was used to determine the validity of the variables. The eight variables examined in this case are gender ( : -2.329; p < 0.25), age ( : -0.151; p < 0.25), race ( : 2.504; p < 0.25), coronary heart disease status ( : -0.481; p < 0.25), waist circumference ( : 0.572; p < 0.25), alanine transferase ( : 0.002; p < 0.25), triglycerides ( : 0.046; p < 0.25), and dyslipidemia ( : 30.769; p < 0.25). There is a linear model that has a 9.019188 MSE.lm in this case. Conclusion: This study will develop and extensively evaluate a novel hybrid approach combining bootstrapping and multiple linear regression. The R syntax for this procedure was chosen to ensure that the researcher comprehends the example completely. The statistical methods used to conduct this research study using R show that regression modelling is better than R-squared values for the predicted mean squared error. Thus, the study's conclusion shows that the hybrid model technique is superior. This vital conclusion helps us better understand the hybrid method's relative contribution to the result in this case.
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