Introduction: The end product of purine metabolism in humans is uric acid (UA). Although uric acid can function as either an antioxidant or an oxidant depending on the surrounding environment, the chemical environment can also impact uric acid.The uric acid level in the serum can predict the development of diabetic nephropathy in type 1 diabetes. Objective: This study aims to determine factors that are perhaps having an association with acid uric. Method: Variables selection is basedon clinical importance. The most significant variable will be assigned and analyzed using Artificial Neural Network (ANN) through multilayer feed-forward and contour plot. Results: Through the architecture of MLFF with two hidden layers, it was found that Creatinine level, Urea level, Systolic Blood Pressure reading, Waist circumference reading, Gender play an essential role toward uric level with an accuracy of 97.7% and the predicted mean squared error (MSE.net) is 0.005. The combination of the selected variable showing the highest significance in predicting the level of uric acid. Conclusion: These findings offer useful future management action plans for patients with diabetes.By controlling these four variables can improve the level of health among diabetic patients. Bangladesh Journal of Medical Science Vol.20(4) 2021 p.741-747
Background: COVID-19 outbreak is being studied throughout the world. Adding more analysis to date strengthening the information about the illness. Here, we analysis the data of Malaysian Ministry of Health from February 15, 2020 until January 10, 2021 was analysed using linear regression model statistical analysis with aim to forecast the trend. Materials and Methods: This study reviewed the data by Malaysia Ministry of Health from February 15, 2020, until January 10, 2021. Linear regression model statistical analysis was used for predictive modelling. The forecasting of the linear trend of the Covid-19 outbreak prediction is purposed to estimate the number of confirm cases according to the number of recoveries patients. Results: Malaysia is currently anticipating another lockdown restriction as new confirmed case of COVID-19 hit new record high. The cumulative confirmed Covid-19 cases in MCO predicted a sharp increase. At the first of March, 2021, the predicted cumulative confirmed Covid-19 cases are 319,477 cases. Conclusions: Covid-19 cases projected to 315766 by end of February 2021 with 3000- 4000 daily cases predicted. Initiative and proactive measurement by Malaysian government hopefully can reduce the number of cases and flatten the infection curve. Bangladesh Journal of Medical Science Vol.20(3) 2021 p.504-510
Introduction: Probiotics are well-defined as live microorganisms that usefully affect the host and probiotic bacteria have been used intensely. For years to target gastrointestinal disease by rebalancing the compound microflora. Besides the gastrointestinal tract also the oral cavity is highly colonized by bacteria and many different bacterial species are part of the microbiota in the mouth, as it offers ideal conditions for bacteria with a stable temperature, moist surface with a relatively stable pH and regular supply of nutrients. Probiotic bacteria like Lactobacillus are a promising treatment strategy for oral disease with a microbiological etiology. To gain better results, many researchers that study and emphasize specific methods been tried to build a new or improved methodology. Objectives: The aimed of this study is to improve the performance of exponential growth by adding bootstrap and fuzzy techniques (Integrated exponential regression method). The aim of the research work is to develop a comprehensive framework for an integrated exponential regression model. Material and Methods: The data were taken from the present data available from the recently done by a researcher for nurturing selected microorganisms. The gathered data will be used for the exponential modeling and the efficiency of the model will be compared accordingly due to the predicted interval from the exponential regression method and an integrated exponential regression method. This paper also provides the algorithm for the prediction of cell growth and inferences. Results: The result shows that the average width for the exponential regression model was 19.2228 while an integrated exponential regression method was 0.0075. The average width of integrated exponential regression was smaller than the exponential regression. This clearly shows that the integrated exponential regression method is more efficient than exponential regression technique. Conclusion: This proposed method can be applied to small sample size data, especially when limited data is obtained. Bangladesh Journal of Medical Science Vol.19(3) 2020 p.552-557
Oral and maxillofacial fractures are the most common injuries among multiple trauma. About 5% to 10% of trauma patients having facial fractures. The objectives of this case study are to focus the most common mid-face fractures types' and to determine the relationship of the midface fracture in maxillofacial trauma among the patient who attended the outpatient clinic in a Hospital Universiti Sains Malaysia. In this research paper, an advanced statistical tool was chosen through the multilayer perceptron neural network methodology (MLPNN). Multilayer perceptron neural network methodology was applied to determine the most associated predictor important toward maxillary bone injury. Through the predictor important classification analysis, the relationship of each bone will be determined, and sorting according to their contribution. After sorting the most associated predictor important toward maxillary bone injury, the validation process will be applied through the value of training, testing, and validation. The input variables of MLPNN were zygomatic complex fracture, orbital wall fracture, nasal bone fracture, frontal bone fracture, and zygomatic arch fracture. The performance of MLPNN having high accuracy with 82.2%. As a conclusion, the zygomatic complex fracture is the most common fracture trauma among the patient, having the most important association toward maxillary bone fracture. This finding has the highest potential for further statistical modeling for education purposes and the decision-maker among the surgeon.
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
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.