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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]

Introduction: The number of patients who present with facial injuries every year is on the rise. Most admission requires combined intervention by neurosurgery and maxillofacial team due to frontal bone fractures associated with various types of brain injury. The most common form of skull bone fracture is a frontal bone fracture. A high-impact head injury can fracture the frontal bone and other nearby bones. Objectives and Method: There is a retrospective study of patients with maxillofacial trauma at Hospital Universiti Sains Malaysia (USM) over five years (1 January 2012 to 31 December 2016). The hospital records of patients who sustained these fractures were analyzed using the newly developed R syntax. This study aims to determine which facial bone fractures are associated with a frontal bone fracture in maxillofacial trauma that occurs at the same time. Therefore, this study proposes an application of Artificial Neural Networks (ANNs) through a feed-forward network toward clinical study data on craniofacial fractures. The most associated bones related to the frontal bone fracture will be determined and will be the input for the multiple logistic regression (MLR). The analysis will be conducted entirely using developed R syntax. The generated syntax is divided into three major sections: Bootstrap (B), Multilayer Perceptron (MLP), and Multiple Logistic Regression. Results: This type of fracture occurred in 218 patients, with 80.7% male and 19.3% female. There is four variable which was Gender (ß1 =1.031; p 0.25; 95% CI:1.028,7.658), Le Fort III fracture (2 ß =1.175; p 0.25; 95% CI: 0.831,12.628), mandibular symphysis fracture (3 ß = -0.935; p 0.25; 95% CI: 0.115,1.342), and mandibular condylar fracture (4 = -1.485; p 0.25; 95% CI: 0.028,1.844). The above MLP gave the lowest mean absolute deviance (0.0007179404). The accuracy obtained is about 99.928%. Conclusions: A Multilayer Feed-Forward Neural Network (MLFF) with multiple logistics regression for the modeling and prediction purpose of collected data is a good approach. The result obtained is being tested and checked from an important clinical point of view. This approachable technique was discovered to have superiority in the variable selection for multiple logistic regression modeling. In real life, many of the relationships between inputs and outputs are non-linear as well as complex relationships. As a result, using MLFF for variable selection, especially for modeling purposes, is a very good strategy and was discovered to have superiority of the variable selection for multiple logistic regression modeling. [ABSTRACT FROM AUTHOR]

Introduction: General anaesthesia (GA) dental care is one of the clinical strategies used to treat non-cooperative those, patients with chronic medical problems or with specialised and comprehensive treatment by some paediatric dentists. Objective: The purpose of this retrospective research was to analyse cases of general anaesthesia in paediatric dentistry at Hospital Universiti Sains Malaysia (USM), Kubang Kerian, Kelantan. Methods: A total of 298 patients reports were collected for data processing from 2015 to 2018. Results: About 54% of patients in the Malay ethnic community were male and the mean age was 5 years. The highest treatment is on the fissure sealant restoration, 100(33.6%) and follows by extraction of deciduous teeth 218(73.2%). The lowest treatment was found in Sandwich Technique Restoration 1(0.3%), excision of chronic mucocele, which is about 2(0.7%), and the treatment based on GIC Fuji IV 2(0.7%). The next analysis is focusing on the type of treatment. The result from multiple responses shows that patients with a combination of three treatment having 61%, this is the highest percentage. While patients with four types of treatment are the second highest, 59 cases or 25.4% and the third highest comes from the category of patients with two types of treatment. Conclusion: An annual rise in referred cases for dental care under GA has been observed. it is believed that the number of patients receiving dental treatment under GA is likely will continue to show an upward trend, and for the specific finding it was found that the extraction deciduous teeth are the highest case which is about 31.2%, fissure sealant restoration about 14.3% and stainless steel crown which is 13.3%. [ABSTRACT FROM AUTHOR]

Objective: This study aims to find the association of fractured orbital walls with other possible fractures reported in the maxillofacial trauma cases in the Oral Maxillofacial Clinic Oral Maxillofacial ward, Hospital USM Kelantan, Malaysia. Materials and methods: From 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. Data were obtained from 294 patients who met the study's eligibility requirements. The medical records of every patient with a comprehensive medical history were reviewed. The following factors were studied: age, gender, zygomatic arch, maxillary sinus, orbital wall, symphysis of the mandible, parasymphysis, and the condyle. The broken orbital walls in these patients were examined in detail. In the first stage, all of the variables that have been picked will be assessed for their significance from a clinical standpoint. All potential factors contributing to the orbital wall fracture were analyzed using the SPSS and R studio programs. As a result of meeting the inclusion criteria, 294 patients' data has been gathered. Each patient who had a complete medical record was subjected to an examination. In these patients, the cracked orbital walls were examined in greater depth. All chosen variables will be tested in the first stage to see if they are clinically significant. Results: The participants in this study were 228 men (77.6%) and 66 women (22.4%). It was found that the most common age ranges are 11-20 years (39.8%), 21-30 years, and 31-40 years (26.2%). According to Spearman correlation, all of the studied variables have a significant accosiation, with a p-value of less than 0.05. According to the findings of the multiple logistic regression, it was discovered that gender is significant, [0.2652 (0.1761); p < 0.25], Zygomatic Arch fracture, [ 3 β (SE)= -0.4511(0.2403); p < 0.25], Maxillary Sinus, [ 4 β (SE)= -0.5917 (0.2403); p < 0.25], Symphysis of the mandible, [ 5 β (SE)= 2.4826 (0.7298); p < 0.05], the condyle of the mandible, [ 5 β (SE) = 0.9479 (0.4315); exp (0.9479) = 2.58 = 3 times], the body of the mandible, [ 5 β (SE)= 0.4893 (0.4315); p < 0.25] and the angle of the mandible, [ 5 β (SE) = 0.6911 (0.4286); p < 0.25]. The validation of the factor through the Multilayer Neural Network (MLNN) and the accuracy obtained 97.71% with the predicted mean square error (PMSE) 0.159%. Conclusion: The matrix spearman correlation, multiple logistic regression, and neural network uncovered a clear association between orbital wall fracture and several other parameters. This discovery will help researchers understand the most common orbital wall fracture causes in maxillofacial trauma. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to develop and illustrate an optimum variable selection approach using Multiple Linear Regression (MLR) and validation using Multi-Layer Feed Forward Neural Network (MLFF), also known as Multilayer Perceptron Neural Network (MLP) while taking bootstrapping into account. All of the factors specified will be evaluated to determine if they have a meaningful association. Objective:The goal of this study was to create a new method in making predictions for oral health data by combining a few statistical techniques. This would make the model more accurate. Material and Methods:A set of medical data that consists 30 observations was used to develop the methodology. The data descriptions of variables being used in this retrospective research including Creatinine, Fasting Blood Sugar, Haemoglobin A1C, and Urea were evaluated using advanced computational statistical modelling approaches. The medical data are used to test the R syntax that is developed in this study. The statistics for each sample are calculated using a model that incorporates bootstrapping and multiple linear regression techniques. Results:The statistical strategy which combines Bootstrap, MLFF and MLR is better than regular statistical method being used for this type of data. The hybrid model technique's accuracy was increased when the data was separated into training and testing dataset. Four variables are taken into account in this case: fasting blood sugar, creatinine, haemoglobin A1C, and urea. Fasting Blood Sugar (β1: 0.461889; p< 0.05), Creatinine (β3: 0.029761; p< 0.05) and Urea (β2:-0.454766; p< 0.05) are all significant, whereas MLFF and MLRhave the predicted mean square error (PMSE) values of 0.01226 and 0.3531 respectively. Conclusion: The value of PMSE is mainly used to diagnose the performance of MLR and MLFF. The MLFF is used to determine how close predicted values are to real data, while the model that has low PMSE value indicates that the model is accurate. The R syntax for combining Bootstrap, MLR, and MLFF is also included in this research article. In a word, this research establishes the superiority of the hybrid model method. [ABSTRACT FROM AUTHOR]

Introduction: One of the major risk factors for cardiovascular disease in diabetes mellitus is dyslipidaemia. The characteristic of diabetic dyslipidaemia includes high plasma triglycerides concentration, low HDL cholesterol concentration and increased the concentration of small dense LDL-cholesterol particles. Most dyslipidaemias are hyperlipidemias; which is an increase in lipids in the blood that occur in developed countries. This is often caused by diet and lifestyle. Prolonged levels of insulin can also cause dyslipidaemia. Objective: The aim of this paper is to determine the factor that contributes to dyslipidaemia disease among Type 2 Diabetes Mellitus attending the outpatient clinic in Hospital Universiti Sains Malaysia. Method: The first method approach for the factor determination is through binary logistic regression (BLR). After obtaining the list of factors, the validation process will take the place. The validation of the factor will be obtained through multilayer perceptron (MLP) procedure where the training and testing procedure will be applied. Results: The accuracy and the significant factor will be determined and used for educational purposes and for the decision maker. Through the both methodology it was found that gender (β3 = -2.643, p < 0.25, 95% CI: 0.004, 1.353), sodium (β2 = -0.661, p < 0.25, 95% CI: 0.313, 0.850), and creatinine, (β1 = 0.043, p < 0.25, 95% CI: 0.997, 1.093), were the factor that contribute most to the dyslipidaemia disease. Conclusion: From these two methodologies, it was found that creatinine, sodium and gender factor are the most dominating factor which contributes to dyslipidaemia disease. These three factors were validated through the multilayer perceptron (MLP) procedure. [ABSTRACT FROM AUTHOR]

Background and Objectives: According to the global cancer situation, which is very alarming, with over 10 million new diagnoses and more than 6 million deaths each year globally, cancer is one of the most prominent causes of morbidity and mortality today. One of the cancers is oral cancer. Oral cancer is the irregular development of malignant cells in the oral cavity. The study's objective was to decide the mortality of cross-tabulation among patients treated for oral carcinoma from Hospital Universiti Sains Malaysia (USM), Kelantan, Malaysia. Materials and Methods: This chapter summarizes the medical history for 7 years from January 2011 to December 2018 of patients who have been treated for oral carcinoma in the Hospital USM, Oral and Maxillofacial Surgery (OMFS) Unit. Each patient's complete medical record was checked, and data gathered were based on age, gender, site lesion, clinical diagnosis, and mortality. Version 26.0 of the SPSS software was used to evaluate the correlation and distribution of patient survival. Results: This was a retrospective cross-sectional review of the medical evidence of 117 patients infected for oral carcinoma at OMFS (Hospital USM). Sixty-seven (57.26%) of the patients were male and fifty (42.74%) were female. Patient age ranged from 25 to 93 years. Malay has the highest prevalence (85.5%) in oral carcinoma, followed by a second ethnic group, Chinese (7.7%). The result indicates that the majority of oral carcinoma patients were over 60 years old. Cases of oral squamous cell carcinoma have proved to be the most prevalent malignant tumour in the mouth cavity. The largest number of cases collected is 91% of the data collected. Mucoepidermoid carcinoma (10%) is the second most common small salivary gland tumor. Conclusion: OSCC is the most prevalent kind of oral cancer. According to the data review, the most popular site for oral cancer is the tongue. [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]

Introduction: Breastmilk alone is the best food and drink for an infant for the first six months of life. After six months, infants need other nutritious foods, in addition to breastfeeding up to two years and beyond, to meet their growth and development needs. Objective: This study is conducted to determine the knowledge, attitude, and future exclusive breastfeeding intention among final year medical and dental students in University Sains Malaysia Method: Multiple Response Analysis methodologies were used to determine the level of acceptance. The seven domains of knowledge were analyzed to determine the most dominant factor which leads to the acceptance of future intention towards exclusive breastfeeding. The co-occurrence frequency network analysis was used to determine the most frequent event between the studied factors. Results: All the findings from this analysis will be used as an indicator for the general knowledge and attitude towards exclusive breastfeeding focusing on young adults. Conclusion: This study found that most final year medical students in Universiti Sains Malaysia generally have positive future intentions to practice exclusive breastfeeding compared to dental students. [ABSTRACT FROM AUTHOR]

Introduction: Oral cancer is a disease resulting from an abnormal growth cell found in the mouth region such as in lips, tongue or throat. Furthermore, oral cancer commonly occurs at the buccal mucosa (cheek), tongue, the floor of the mouth and lip. The previous study shows that many studies focus especially on detecting the factors with the highest probability of cancer but the study which emphasizes the prediction of the tumor size category is still less from the point of computational biostatistical. Objective: The aim of this research paper is to build the methodology for computing ordinal regression model through SAS language by considering the clinical pathological prognostic factors in patients who attended Hospital Universiti Sains Malaysia (HUSM) from 2005 to 2015 (based on secondary data). Method: In this paper, there are three main methodologies proposed in this study. The first and second methodology is on the ordinal regression methodology and followed by bootstrap method, and the second is a nonlinear programming (NLP) methodology, which aims to obtain fuzzy regression modeling for the prediction purposed. Results: The result from ordinal regression had shown that smoking and nerve invasion factors contributing significantly to the growth of a tumor. The significant result from this finding (output based) can be used to educate people or stakeholder of how important this factor toward patients management. Conclusion: From a statistical point of view, the integration of computing methodology can expose and provide the researcher with an integrated method of analysis. [ABSTRACT FROM AUTHOR]

Introduction: Breastfeeding has many health benefits, both in the short term and the long term either to infants and their mothers. There is an increasing number of studies that report on associations between breastfeeding and long-term protection against chronic disease. Objective: To determine the factor that contributes to the future intention of exclusive breastfeeding among final year medical and dental students in Universiti Sains Malaysia (USM) Method: Multilayer Perceptron (MLP) methodology is being applied for assessing the factor which might contribute to the future intention of exclusive breastfeeding. After obtaining the list of the factor from the predictor important, the validation process will take the place through training and testing assessment. The accuracy and significant factor will be determined, and the finding will be used for education purposes and for further research. Results: There are four factors that contribute most to the future intention of exclusive breastfeeding. The main contributer factor to the future intention of exclusive breastfeeding among final year medical and dental students was ethnicity (48%), gender (19%), school (19%) and marital status (14%). Conclusion: The proposed model is very useful for the prediction and for the inferences of patient's management time with the high-risk breastfeeding contributor factors. [ABSTRACT FROM AUTHOR]

Introduction: Dyslipidaemia is one of the main risk factors for coronary heart disease with diabetes mellitus. Diabetic dyslipidaemia is associated with a decreased concentration of HDL cholesterol, high concentration of plasma triglyceride, and increased small dense LDL-cholesterol particles concentration. Objective: The aim of this paper is to study the blood pressure, dyslipidaemia characteristics towards biochemical profiles among patients with type 2 diabetes mellitus which attending the outpatient clinic in Hospital Universiti Sains Malaysia. Method: The biochemical profile will be checked for the differences according to the dyslipidaemia characteristics and status of hypertension using independent t-test. All the significant variables which obtained from the first analysis will be used for contour and surface plot analysis. Through these methodologies, the behaviour of significant variables will be illustrated and estimated carefully. Results: Those patients with and without dyslipidaemia problem, only three factors show the significant different, which referred as triglycerides, creatinine and sodium. Those patients with and without hypertension problem, only fasting blood sugar, high-density lipoprotein (HDL) and urea reading had showed the result of quite significant differences. Thus, patient with dyslipidaemia characteristics usually having high level of sodium. High sodium (138.74 mEq/L) intake contributed to increased etiology of dyslipidaemia, high-density lipoprotein (HDL) hypercholesterolemia, and a risk of being overweight. The creatinine level shows the significant differences among a patient with and without dyslipidaemia. Patients without dyslipidaemia disease having the level of creatinine, 107.80 (28.54) μmol/L compared to 81.52 (34.65) μmol/L. Triglycerides level among dyslipidaemia in patient with type 2 diabetes patients is elevated triglyceride level and decreased HDL cholesterol levels. Conclusion: The utmost finding from this study, it provides a very useful information to the diabetic patients for future management action plan. [ABSTRACT FROM AUTHOR]

Objective: This study has been done to assess Malaysia's effectiveness in handling COVID-19 compared to other countries in South East Asia. Methods: Stochastic Frontier Analysis is capable of compiling the effectiveness of dealing with COVID-19 outbreaks according to its function and not using a specific distribution function. The highest technical efficiency score is the most efficient and reflects the ability of the country to deal with the COVID-19 outbreak, which is very well without any problems. Results: Thailand was shown to be ranked 1 (TE = 0.88341) with a technical efficiency score higher than most other countries. The score for second place is Malaysia with a technical efficiency score of TE = 0.88338, the third place score is Indonesia (TE = 0.83342) and last position is Philippine (TE = 0.67706). Conclusions: Ministry of Health took the implementation of MCO, and action did put Malaysia as the second most effective country in Southeast Asia in managing COVID-19 infection. This data hopefully, could benefit Malaysia and all other countries to handle this COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]