Prediction and Elucidation of Triglycerides Levels Using a Machine Learning and Linear Fuzzy Modelling Approach.

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.

The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation.

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 (ß 1: -0.006423; p < 2e - 16), treatment (ß 2: -0.355389; p < 2e - 16), and distant metastasis (ß 3: -0.355389; p < 2e - 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.

A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression.

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, α = 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.

The Most Common Relationship of a Midface Fracture in Maxillofacial Trauma Study.

ABSTRACT: 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.

A Comprehensive Cross-Tabulation Analysis of Oral Carcinoma Patients: A Retrospective Study of Recent 7 Years.

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.

Modeling the Growth of Bacteria Streptococcus sobrinus Using Exponential Regression

Abstract Objective: To build an exponential regression model based on parameter estimation. Material and Methods: We developed a simple mathematical model to simulate the growth of bacteria and the exponential growth is often used to model population growth as such cell growth while the exponential decay is portraying a declining or decreases in the size of the population. An exponential regression method was used to fit the data and estimate growth parameter values Streptococcus sobrinus using statistical software SPSS version 20. Results: Based on the results of the parameter estimates, which is constant are 83.039 and b1 is 0.005 while R-square is 0.952. According to the R-Square results obtained, the model is good and appropriate. Conclusion: The model can be used to find the potential biological parameters, which may be able to predict the treatment outcome. This study helps researchers to understand the specific growth rate(s), which can be used to best grow the organism.