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
Abstract:Objectives: To determine the prevalence of dental anomalies, sexual dimorphism and antimere differences of tooth size of Malay in Malaysia. Methods: Orthodontic patients for the years 2008-2010 were selected. Among these two hundred patients' were selected based on file records. Their panoramic radiographs were examined.. The prevalence of various dental anomalies was determined. Mesiodistal and buccolingual diameters of the teeth were measured using electronic calipers with accuracy of up to 0.01mm. Analysis was carried out using SPSS statistical package version 18.0 (2009). Results: In the Malay patients the frequency of hypodontia was 7.5%, followed by hyperdontia (2%), microdontia, dens evaginatus and short root were 1%, respectively. In addition, their macrodontia, germination and dilaceration were 0.5% , while the remaining 86% did not display any dental anomalies. This study demonstrated greater tooth sizes in male compared to female subjects except for buccolingual site of upper canine and lower incisors. Greatest dimorphism in mesiodistal dimension was noted in the lower canine while buccolingual dimension was presented by upper lateral incisor. It was found that there was no significant difference (P>0.05) in tooth measurements for right and left antimeres observed for the majority of tooth classes. Conclusion: In the Malay subjects, hypodontia was the commonest dental anomaly. The Malay males had greater tooth sizes than their female counterparts. There were almost no significant antimere differences in tooth sizes.
Background. Cancer is primarily caused by smoking, alcohol, betel quit, a series of genetic alterations, and epigenetic abnormalities in signaling pathways, which result in a variety of phenotypes that favor the development of OSCC. Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for 80–90% of all oral malignant neoplasms. Oral cancer is relatively common, and it is frequently curable when detected and treated early enough. The tumor-node-metastasis (TNM) staging system is used to determine patient prognosis; however, geographical inaccuracies frequently occur, affecting management. Objective. To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. 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 methods such as bootstrap and multiple linear regression (MLR). Results. The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age ( β 1 : -0.006423; p < 2 e − 16 ), treatment ( β 2 : -0.355389; p < 2 e − 16 ), and distant metastasis ( β 3 : -0.355389; p < 2 e − 16 ). There is a 0.003469102 MSE for the linear model in this scenario. Conclusion. In this study, a hybrid approach combining bootstrapping and multiple linear 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 better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.
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