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Introduction: Chronic kidney disease (CKD) has become more common in recent decades, putting significant strain on healthcare systems worldwide. CKD is a global health issue that can lead to severe complications such as kidney failure and death. Objective: The purpose of this study was to investigate the actual causes of the alarming increase of kidney failure cases in Saudi Arabia using the supersaturated design analysis and edge design analysis. Materials and methods: A cross-sectional questionnaire was distributed to the general population in the KSA, and data were collected using Google Forms. A total of 401 responses were received. To determine the actual causes of kidney failure, edge and supersaturated designs analysis methods were used, which resulted in statistical significance. All variables were studied from factor h1 to factor h18 related to the causes of kidney failure. Results: The supersaturated analysis method revealed that the reasons for the increase in kidney failure cases are as follows: h9(Bad diet), h8(Recurrent urinary tract infection), h1 (Not drinking fluids), h6 (Lack of exercise), h14 (drinking from places not designated for valleys and reefs), h18 (Rheumatic diseases), h10 (Smoking and alcohol consumption), h13 (Direct damage to the kidneys), h2 (take medications), h17 (excessive intake of soft drinks), h12 (Infection), h5 (heart disease), h3 (diabetes), h4 (pressure disease), h15 (Dyes used in X-rays), and h11 (The presence of kidney stones) are all valid. The design analysis method by edges revealed that the following factors contributed to an increase in kidney failure cases: h8 (Recurrent urinary tract infection), h6 (Lack of exercise), h7 (Obesity), and h11. Conclusion: The findings showed that there were causes of kidney failure that led to the statistical significance, which is h8 (Recurrent urinary tract infection) and h11 (The presence of kidney stones) [ABSTRACT FROM AUTHOR]

The objective of this research is to ascertain the elements that have an impact on and drive domestic violence in Saudi Arabia, a phenomenon that has a prevalence rate of around 35% among women globally. The researchers administered a survey to a sample of 550 individuals and used exploratory factor analysis (EFA) to analyze the collected data. The findings revealed three factors: a lack of familial unity, encouragement of detrimental characteristics, and economic turmoil. The authors examined the consequences of these characteristics on preventive and intervention programs and proposed suggestions for policymakers and researchers. This research enhances the existing body of knowledge on domestic violence by conducting a statistical analysis to examine the factors that lead to it and the resulting outcomes within a particular cultural setting. [ABSTRACT FROM AUTHOR]

The core objective of this work is to predict stock market indices' using autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and their combination in the form of ARIMA-ANN. Financial data are, in fact, trendy, noisy and highly volatile. To tackle their chaotic nature and forecast the three considered stock markets, namely Nasdaq stock exchange, United States, Nikkei stock exchange, Japan, and France stock exchange data (CAC 40 index), we use novel approaches. The data are taken from the Yahoo Finance website for the period from 4 January 2010 to 20 August 2021. To assess the relative predictive effectiveness of the selected tools, the dataset was divided into two distinct subsets: 75% of the data was allocated for training purposes, while the remaining 25% was reserved for testing. The empirical results suggest that ARIMA-ANN produces more accurate forecasts than the separate components of all stock markets. In light of this, it may be inferred that the combining tool is more effective in analyzing financial data and provides a more accurate comparative prediction. [ABSTRACT FROM AUTHOR]

The modeling of pandemics is significant in understanding and addressing the spread of infectious diseases. This study introduces a novel and highly flexible extension of the asymmetric unit Burr–Hatke distribution, termed the power Burr–Hatke distribution (PUBHD), and comprehensively investigates its mathematical properties. Multiple parameter estimation methods are employed, and their asymptotic behavior is analyzed through simulation experiments. The different estimation techniques are compared to identify the most efficient approach for estimating the distribution's parameters. To demonstrate the applicability and usefulness of the PUBHD model, we conducted a case study using a sample from the COVID-19 dataset and compared its performance with other established models. Our findings show that the PUBHD model provides a superior fit to the COVID-19 dataset and offers a valuable tool for accurately modeling real-life pandemics. [ABSTRACT FROM AUTHOR]

In this paper, we introduced a novel general two-parameter statistical distribution which can be presented as a mix of both exponential and gamma distributions. Some statistical properties of the general model were derived mathematically. Many estimation methods studied the estimation of the proposed model parameters. A new statistical model was presented as a particular case of the general two-parameter model, which is used to study the performance of the different estimation methods with the randomly generated data sets. Finally, the COVID-19 data set was used to show the superiority of the particular case for fitting real-world data sets over other compared well-known models. [ABSTRACT FROM AUTHOR]

The power XLindley (PXL) distribution is introduced in this study. It is a two-parameter distribution that extends the XLindley distribution established in this paper. Numerous statistical characteristics of the suggested model were determined analytically. The proposed model's fuzzy dependability was statistically assessed. Numerous estimation techniques have been devised for the purpose of estimating the proposed model parameters. The behaviour of these factors was examined using randomly generated data and developed estimation approaches. The suggested model seems to be superior to its base model and other well-known and related models when applied to the COVID-19 data set. [ABSTRACT FROM AUTHOR]

The frequency and timing of antenatal care visits are observed to be the significant factors of infant and maternal morbidity and mortality. The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least square regression model. A data set collected during 2017-2018 by Pakistan Demographic and Health Surveys is used for modeling purposes. The partial least square regression model coupled with rank correlation measures are introduced for improved performance to address ranked response. The proposed models included PLS ρ s , PLS τ A , PLS τ B , PLS τ C , PLS D , PLS τ G K , PLS G , and PLS U . Three filter-based factor selection methods are executed, and leave-one-out cross-validation by linear discriminant analysis is measured on predicted scores of all models. Finally, the Monte Carlo simulation method with 10 iterations of repeated sampling for optimization of validation performance is applied to select the optimum model. The standard and proposed models are executed over simulated and real data sets for efficiency comparison. The PLS ρ s is found to be the most appropriate proposed method to model the observed ranked data set of antenatal care visits based on validation performance. The optimal model selected 29 influential factors of inadequate use of antenatal care. The important factors of reduced antenatal care visits included women's educational status, wealth index, total children ever born, husband's education level, domestic violence, and history of cesarean section. The findings recommended that partial least square regression algorithms coupled with rank correlation coefficients provide more efficient estimates of ranked data in the presence of multicollinearity. [ABSTRACT FROM AUTHOR]

Background. In the modern contemporary, there are obvious demands for accurate interpretations of the worldwide problem, which is medication errors (MEs) due to various serious negative events that effect patient health. Most parts of the world considered health as significant issue for centuries. Recently, investigators have examined the effects of writing physician orders from the nurse's viewpoint and represent that 100% of ambiguous writing of doctor orders related directly to MEs. Objective. The aim of our work is to investigate the major causes of (MEs) in the Saudi Arabia population from multiple aspects. Methods. An online review gave quantitative information from 450 members. Respondents were heedlessly parceled into two conditions (Yes+, No−) and mentioned to respond to one of two plans of the explanations behind the medical errors. Fourteen determining factors in the predesign have been chosen. Entire data were collected relevant to the study purpose and the content of the questionnaire written suitably to the participants with no ambiguous terms to analyze obtained data accurately using supersaturated plans and regression methods utilizing the SPSS program to decide the real causes of the medical errors. Results. The findings indicated that often failures in the care process can be traced back to poor documentation and a lack or inadequacy of procedures; the limitations of integrated health systems between the doctor and pharmacists, human problems when standards of care, policies, processes, or procedures are not properly or effectively followed, inadequate use of technology in healthcare facilities, and unclear line of prescription from the doctor are factors that contributed to the medical errors. Conclusion. The Saudi Arabian government needs to foster a functional arrangement to examine these reasons for medical errors and make a move. Future investigations could break down the information utilizing edge plans technique. [ABSTRACT FROM AUTHOR]

Introduction. According to the World Health Organization (2020), obesity is a growing problem worldwide. In fact, obesity is characterized as an epidemic. Objective. The aim of this paper is to use a logistic regression model as one of the generalized linear models and decision tree as one of the machine learning in order to assess the knowledge of the risk factors for obesity among citizens in Saudi Arabia. Methods and Materials. A cross-sectional questionnaire was given to the general population in KSA, using Google forms, to collect data. A total of 1369 people responded. Results. The findings showed that there is widespread knowledge of risk factors for obesity among citizens in Saudi Arabia. Participants' knowledge of risk factors was very high (95.5%). In addition, a significant association was found between demographics (gender, age, and level of education) and knowledge of risk factors for obesity, in assessing variables for knowledge of the risk factors for obesity in relation to the demographics of gender and level of education. In addition, from decision tree results, we found that level of education and marital status were the most important variables to affect knowledge of risk factors for obesity among respondents. The accuracy of correctly classified cases was 95.5%, the same in logistic regression and decision tree. Conclusion. The majority of participants saw regular exercise and diet as an essential way to reduce obesity; however, awareness campaigns should be maintained in order to avoid complacency and combat the disease. [ABSTRACT FROM AUTHOR]

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution. [ABSTRACT FROM AUTHOR]

Background. Increasing divorce rates is a major problem in Saudi society. Divorce has become the primary solution for many couples who are experiencing problems in their relationships, and the language of divorce has become prevalent in both the daily register of Saudi courts on a daily basis and in fictional works. This trend has become a threat to marital life and is particularly damaging for children and young people. There has been previous research on the increase in divorce rates; however, no one addresses this important issue statistically. The specific research question is what are the actual reasons for the increase in divorce rates mentioned in previous studies? Objective. This study contains a statistical analysis of the underlying reasons for rising divorce rates in Saudi society. Methods. An online survey was conducted that gathered quantitative data from 800 participants. The responses of factual relevance were examined via edge plan analysis, regression analysis, and analyses with SPSS software to determine the causes for the rise in divorces in Saudi society. A predesign of edge consisting of six factors and twelve trials is selected. The design is examined in all the data for each row of it and according to the factor or reason chosen, provided that all design items are fulfilled; after that, it is analyzed in two ways. Results. Some examples are presented, so the findings indicated that differences on the couple's characters and the length of time the wife spent doing housework and amount of attention to the husband are factors that contributed to divorce. Conclusion. The Saudi Arabian government needs to develop an operational plan to study these causes of divorce and take action. Future studies could analyze the data using supersaturated prototypes, where a large number of variables are studied in just a few simulation trials. [ABSTRACT FROM AUTHOR]

Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets. [ABSTRACT FROM AUTHOR]

Extropy, as a complementary dual of entropy, has been discussed in many works of literature, where it is declared for other measures as an extension of extropy. In this article, we obtain the extropy of generalized order statistics via its dual and give some examples from well-known distributions. Furthermore, we study the residual and past extropy for such models. On the other hand, based on Farlie–Gumbel–Morgenstern distribution, we consider the residual extropy of concomitants of m-generalized order statistics and present this measure with some additional features. In addition, we provide the upper bound and stochastic orders of it. Finally, nonparametric estimation of the residual extropy of concomitants of m-generalized order statistics is included using simulated and real data connected with COVID-19 virus. [ABSTRACT FROM AUTHOR]

Factor discovery of public health surveillance data is a crucial problem and extremely challenging from a scientific viewpoint with enormous applications in research studies. In this study, the main focus is to introduce the improved survival regression technique in the presence of multicollinearity, and hence, the partial least squares spline modeling approach is proposed. The proposed method is compared with the benchmark partial least squares Cox regression model in terms of accuracy based on the Akaike information criterion. Further, the optimal model is practiced on a real data set of infant mortality obtained from the Pakistan Demographic and Health Survey. This model is implemented to assess the significant risk factors of infant mortality. The recommended features contain key information about infant survival and could be useful in public health surveillance-related research. [ABSTRACT FROM AUTHOR]