The Evaluation of Ordinal Regression Model's Performance Through the Implementation of Multilayer Feed-Forward Neural Network: A Case Study of Hypertension.

Background Hypertension, or high blood pressure, is a common medical condition that affects a significant portion of the global population. It is a major risk factor for cardiovascular diseases (CVD), stroke, and kidney disorders. Objective The objective of this study is to create and validate a model that combines bootstrapping, ordered logistic regression, and multilayer feed-forward neural networks (MLFFNN) to identify and analyze the factors associated with hypertension patients who also have dyslipidemia. Material and methods A total of 33 participants were enrolled from the Hospital Universiti Sains Malaysia (USM) for this study. In this study, advanced computational statistical modeling techniques were utilized to examine the relationship between hypertension status and several potential predictors. The RStudio (Posit, Boston, MA) software and syntax were implemented to establish the relationship between hypertension status and the predictors. Results The statistical analysis showed that the developed methodology demonstrates good model fitting through the value of predicted mean square error (MSE), mean absolute deviance (MAD), and accuracy. To evaluate model fitting, the data in this study was divided into distinct training and testing datasets. The findings revealed that the results strongly support the superior predictive capability of the hybrid model technique. In this case, five variables are considered: marital status, smoking status, systolic blood pressure, fasting blood sugar levels, and high-density lipoprotein levels. It is important to note that all of them affect the hazard ratio: marital status (ß1, -17.12343343; p < 0.25), smoking status (ß2, 1.86069121; p < 0.25), systolic blood pressure (ß3, 0.05037332; p < 0.25), fasting blood sugar (ß4, -0.53880322; p < 0.25), and high-density lipoprotein (ß5, 5.38065556; p < 0.25). Conclusion This research aims to develop and extensively evaluate the hybrid approach. The statistical methods employed in this study using R language show that regression modeling surpasses R-squared values in predicting the mean square error. The study's conclusion provides strong evidence for the superiority of the hybrid model technique.

Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate-A Systematic Review.

OBJECTIVE: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved. MATERIALS AND METHODS: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied. RESULTS: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis. CONCLUSIONS: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

A Randomized Controlled Trial Evaluating the Levels of the Biomarkers hs-CRP, IL-6, and IL-8 in Patients with Temporomandibular Disorder Treated with LLLT, Traditional Conservative Treatment, and a Combination of Both.

Temporomandibular disorders (TMDs) are a type of idiopathic orofacial pain. Inflammation, particularly elevated circulating levels of high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and interleukin-8 (IL-8), has been linked to pain symptoms. The purpose of this study was to compare hs-CRP, IL-6, and IL-8 biomarkers and pain intensity with different treatment strategies (LLLT, standard conservative treatment, and combination) for TMD patients. METHODS: A total of 32 participants were randomly included in the study and divided into three groups (Group I, Group II, and Group III) referred from the Dental Clinic, School of Dental Science, HUSM. Patients received LLLT (Groups II and III) in five sessions for the duration of 10 days. Patients in Groups I and III received standard conservative TMD treatment (diet and stress counseling, jaw exercises, physical therapy, which was a hot towel application) by the principal investigator. All blood samples for biomarkers were performed before starting treatments and directly after finishing the treatment protocols, where all results were recorded. RESULTS: The result showed a significant difference in the mean IL-8 (p = 0.001) between the three intervention groups (LLLT, standard treatment, and combined treatment). IL-6 showed an increase in the mean of IL-6 levels from baseline to post-treatment with a better mean in the LLLT treatment group without any significant differences. Additionally, there were no significant mean differences found between the groups and in the group for the hs-CRP biomarker. CONCLUSIONS: A statistically non-significant difference was found in hs-CRP and IL-6 before and after LLLT, conservative, and combined treatment strategies of TMD. A statistically significant difference was observed in the mean levels of IL-8 between the LLLT intervention group and the combined treatment group. Although there was no statistically significant correlation between pain intensity and biomarkers, a statistically significant difference was found in pain intensity before and after LLLT, conservative, and combined treatment strategies. TMJ degeneration could be exacerbated by elevated IL-8 levels. Thus, this can be an important biomarker to mark or identify the painful condition of TMJ.

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.

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.

Patient Satisfaction Towards Dentist-Patient Interaction Among Patients Attending Outpatient Dental Clinic Hospital Universiti Sains Malaysia

ABSTRACT Objective: To determine the satisfaction with the dentist-patient interaction and factors associated with patient satisfaction among patients attending the outpatient dental clinic Hospital Universiti Sains Malaysia (HUSM). Material and Methods: This cross-sectional study involved 229 patients who attended outpatient dental clinic Hospital USM that located in the East Coast region of Malaysia. A self-administered Skala Kepuasan Interaksi Perubatan - 11 (SKIP-11) questionnaire was used to assess the satisfaction towards dentist-patient interaction. Systematic random sampling was applied in this study. The data were analyzed using simple logistic regression analysis to determine the factors associated with patient satisfaction with dentist-patient interaction. Results: The mean age of patients was 32.6 ± 13.9 years, 71.6% of them study up to tertiary level, 31.5% came to for dental check up and 23.6% of them had tooth decay. More than half (64.6%) of the patients were satisfactory with dentist-patient interaction. The satisfaction percentage in the distress relief domain was 60.7%, 56.8% in the rapport domain, and 53.7% in the interaction outcome domain. Satisfaction with dentist-patient interaction was significantly associated with the dentists' characteristics such as age (OR = 0.583, 95%CI 0.44-0.76, p=0.001), gender (OR = 0.386, 95% CI 0.22-0.69, p=0.001) and years of service (OR = 0.294, 95% CI 0.15-0.57, p=0.001). Conclusion: The result showed that slightly more than half of the patients who attended the outpatient dental clinic HUSM were satisfied with the dentist-patient interaction, which was found to be influenced by the characteristics of the dentists. Efforts to improve patient-dentist interaction are recommended to ensure delivery of good quality oral health care.

Effectiveness of the Laser Application in Temporomandibular Joint Disorder: A Systematic Review of 1172 Patients.

Objective: The aim of this systematic review was to evaluate the effectiveness of laser application in temporomandibular joint disorder. Methods: PubMed, SCOPUS, Science Direct, Web of Science, and Google Scholar electronic databases were searched systematically with restricting the languages to only English and year (January 2001 to March 2020), and studies were selected based on the inclusion criteria. Study quality and publication bias were assessed by using the Robvis, a software package of R statistical software. Results: This systematic review included 32 studies (1172 patients) based on the inclusion and exclusion criteria. Most of the studies reported significant reduction of pain by the use of the laser during TMD treatment. Two-thirds of the study (78.13%) found a better outcome comparing with conventional one. According to Robvis, 84.4% of the studies were high methodological studies with low risk of bias. Conclusion: TMD patients suffer with continuous pain for long time even after conventional treatment. Laser therapy shows a promising outcome of pain reduction for TMD patients. Therefore, laser therapy can be recommended for the TMD patients' better outcome. This trial is registered with PROSPERO (CRD42020177562).

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.