The objective of this research was to identify and analyse the correlation between various factors that could impact the likelihood of medical and dental students at Hospital Universiti Sains Malaysia (Hospital USM), Kelantan, Malaysia to intend to exclusively breastfeed in the future. The questionnaire was designed and validated. The validated questionnaire was distributed among medical dan dental students. The study involved 162 participants, comprising 25 (15.4%) students from the dental program and 137 (84.6%) from the medical program. There are 56 (34.6%) male and 106 (65.4%) female involved in this study. At first, the collected data were analysed using MINITAB software through the contour plot and surface plot. Second, the data were analysed using the Spearman correlation. The result from correlation analysis shows that, most of the studied factors related to general knowledge rather than other studied factors. The discovery indicates that one approach to promoting the acceptance of exclusive breastfeeding among medical and dental students is by improving their general knowledge about the topic. The correlation analysis, 3-D plot, and contour plot demonstrate that factors such as gender, marital status, and specific and general knowledge are associated with the intention to exclusively breastfeed in the future. This finding is very important especially for awareness education, and establishing the future exclusive breastfeeding practiced among future parents.

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, [ (SE)= -0.4511(0.2403); p < 0.25], Maxillary Sinus, [ (SE)= -0.5917 (0.2403) ; p < 0.25], Symphysis of the mandible, [ (SE)= 2.4826 (0.7298); p < 0.05], the condyle of the mandible, [ (SE) = 0.9479 (0.4315); exp (0.9479) = 2.58 3 times], the body of the mandible, [ (SE)= 0.4893 (0.4315) ; p < 0.25] and the angle of the mandible, [ (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.

Background: Cholesterol levels in the blood, comprising both LDL and HDL cholesterol, can lead to artery plaque formation and potential blockages. Researchers are studying cholesterol levels in individuals with dyslipidemia and type 2 diabetes mellitus. Objective: The objective of this paper is to utilize the developed methodology to model the factor associated with the total cholesterol status in patients with dyslipidemia and type 2 diabetes mellitus. This undertaking has the potential to improve the prediction of total cholesterol levels among the analyzed patients by integrating comprehensive supplementary data from a statistical standpoint. Material and Methods: The data was collected from Hospital Universiti Sains Malaysia (Hospital USM), using statistical modelling techniques to evaluate data descriptions of numerous variables, including height, total cholesterol, triglyceride levels, low-density lipoprotein (LDL) levels, high-density lipoprotein (HDL) levels, and alkaline phosphatase (ALP) levels. The developed method was implemented and evaluated using the R-Studio, employing a neural network model with bootstrapping method and response surface methodology. Results: Our proposed method showed superior accuracy when dividing data into training and testing sets, offering a more precise prediction. The neural network's mean square error was approximately 0.021, demonstrating high precision. Conclusion: In this study, the proposed model demonstrates the method's capability for the improvement of research methodology. The outcome suggests that the methodology established for this investigation is capable of producing favourable results. The study's final analysis demonstrates that the model technique created for research is preferable.

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%.

The purpose of this study is to demonstrate the best strategy for the variable selection, using the developed Ordered Logistic Regression (OLR) and Multilayer Perceptron Neural Network (MLP). At the first stage, all the selected variables will be a screen for their important relationship point of view through ordered logistics regression and bootstrap methodology. After considering for 1500 of the bootstrapping methods, it was found that smoking factor, total cholesterol factor, and triglycerides come to a significant relationship to the level of hypertension. By considering the level of significance of 0.25 for ordered logistic regression, these three variables are being selected and used for the input of the MLP model. The performance of MLP was evaluated through the Predicted Mean Square Error (PMSE) of the neural network for the (MSE-forecasts the Network). PMSE is used as a measurement of how far away from our predictions are from the real data. The smallest MSE from MLP, indicate the best combination of variables selection in the model. In this research paper, we also provide the R syntax for OLR and MLP better illustration.