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Introduction. Triglycerides are lipids composed of fatty acids that provide energy to the cell. These compounds are delivered to the body's cells via lipoproteins found in the bloodstream. Increased blood triglyceride levels have been associated with high-fat or high-carbohydrate diets. Generally, increased triglyceride levels occur in conjunction with other symptoms that are difficult to notice and recognize. Objectives. The study's goal was to develop and predict the model that could be used to explain the relationship between triglycerides and waist circumference, high-density lipoprotein (HDL), and hypertension status by determining the relationship between triglycerides and waist circumference, HDL, and hypertension status. This model was developed using qualitative predictor variables and incorporated data bootstrapping multilayer perceptron neural networks and fuzzy linear regression. Materials and procedures. This was a public health study that combined retrospective data analysis with methodology development. The medical records of patients who attended outpatient clinics at Hospital Universiti Sains Malaysia (USM) were collected and analyzed. This was to provide a more extensive illustration of the methods developed. Screening and selection of patient data were necessary following the inclusion and exclusion criteria. The patient's medical record was used to obtain triglycerides, high-density lipoprotein (HDL), waist circumference, and hypertension status. Due to the critical nature of the variable, it was chosen to aid the clinical expert. The R-Studio software was used to develop the associated syntax for the hybrid model, which would define the association between the examined variables. The purpose of this study is to create a technique for the clinical trial design that utilizes bootstrapping, Qualitative Predictor Variables (QPV), Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Fuzzy Regression (FR). All analyses were performed using the newly introduced R syntax. The research developed a fuzzy linear model that increased modelling performance by incorporating clinically significant factors and validated variables via Multilayer Perceptron (MLP). Conclusion. The proposed technique for modelling and prediction appeared to be the ideal combination of bootstrap, Multilayer Feed Forward (MLFF) neural network, and fuzzy linear regression. The created syntax is currently being evaluated and validated clinically. For modelling and prediction, the proposed technique looked to be the best, as it incorporated bootstrap, MLFF neural network, and fuzzy linear regression. The established syntax is now being utilized in the clinic to evaluate and validate the outcome. In terms of variable selection, modelling, and model validation, this strategy was superior to earlier approaches for fuzzy regression modelling. [ABSTRACT FROM AUTHOR]

Background/Aim. Epidemiology of maxillofacial fractures (MFF) varies between populations. This study investigated the epidemiology of MFF treated at the Oral and Maxillofacial Surgery (OMFS) Unit, Hospital Universiti Sains Malaysia (USM). Methods. A retrospective review of 473 medical records of patients with MFF treated from June 2013 to December 2015 was conducted. Information on demographic characteristics of patients, aetiology of injury, types of MFF, and treatment was obtained. Descriptive analysis, Pearson's chi-squared test, and multiple logistic regression analysis were conducted. The level of significance was set at 0.05. Results. Most patients treated for MFF were males (82.2%), aged 30 and below (63.1%), and from Malay ethnic (97.4%). Road traffic accident was the most common cause of MFF (83.1%), with motorcycle accident accounting for most injuries (73.6%). Orbital wall fracture was the most frequent MFF type (51.2%). About half of MFF patients (51.4%) were treated conservatively. Patients aged more than 20 years old were at higher odds of sustaining orbital wall fracture (AOR= 1.76; 95% CI: 1.214-2.558; P= 0.003) but were at lower odds of sustaining mandibular fracture (AOR= 0.47; 95% CI: 0.315-0.695; P= 0.001) than patients who are 20 years old and younger. Helmet use among motorcyclists was significantly associated with the nasal, orbital wall, and maxillary sinus wall fractures (P= 0.006, 0.010, and 0.004, respectively). Conclusion. Motorcycle accident was the most common cause of MFF in Kelantan, Malaysia. Ages of patient and helmet use were associated with the type of MFF sustained. This study provides important information to facilitate the planning of MFF prevention strategies among motorcyclists and emphasizes the importance of using a helmet when riding a motorcycle. [ABSTRACT FROM AUTHOR]

Background. Mental illness is a pertinent risk factor related to suicide. However, research indicates there might be underdiagnosis of mental illness in Asian suicide attempters; this phenomenon is concerning. This study explored prediction of diagnosis of mental illness in suicide attempters in Singapore using available variables. Methods. Three years of medical records related to suicide attempters (N = 462) who were admitted to the emergency department of a large teaching hospital in Singapore were subjected to analysis. Of the sample, 25% were diagnosed with mental illness; 70.6% were females and 29.4% were males; 62.6% were Chinese, 15.4% Malays, and 16.0% Indians. Their age ranged from 12 to 86 (M = 29.37, SD = 12.89). All available variables were subjected to regression analyses. Findings. The full model was significant in predicting cases with and without diagnosis of mental illness and accurately classified 79% of suicide attempters with diagnosis of mental illness. Conclusions. The findings were discussed in regard to clinical implications in diagnosis and primary prevention. [ABSTRACT FROM AUTHOR]