logistic regression

One of the most efficient statistical tools for modeling the relationship between a dependent variable and several independent variables is regression‎. ‎In practice‎, ‎observations relating to one or more variables‎, ‎or the relationship between variables‎, ‎may be vague or non-specific‎. ‎In such cases‎, ‎classic regression methods will not have enough capability to model data‎, ‎and one of the alternative methods is regression in a fuzzy environment‎. ‎The fuzzy logistic regression model provides a framework in the fuzzy environment to investigate the relationship between a binary response variable and a set of covariates‎. ‎The purpose of this paper is to attempt to develop a fuzzy model that is based on the idea of the possibility of success‎. ‎These possibilities are characterized {by several} linguistic phrases‎, ‎including low‎, ‎medium‎, ‎and high‎, ‎among others‎. ‎Next‎, ‎we {use a set of precise explanatory variable observations to model the logarithm transformation of‎ "‎possibilistic odds.‎" ‎We assume that the model's parameters are triangular fuzzy numbers.} We use the least squares method in fuzzy linear regression to estimate the parameters of the provided model‎. ‎We compute three types of goodness-of-fit criteria to evaluate the model‎. ‎Ultimately‎, ‎we model suspected cases of Systemic Lupus Erythematosus (SLE) disease based on significant risk factors to identify the model's application‎. ‎We do this due to the widespread use of logistic regression in clinical studies and the prevalence of ambiguous observations in clinical diagnosis‎. ‎Furthermore‎, ‎to assess the prevalence of diabetes in the community‎, ‎we will collect a sample of plasma glucose levels‎, ‎measured two hours after a meal‎, ‎from each participant in a clinical survey‎. ‎The proposed model has the potential to rationally replace an ordinary model in modeling the clinically ambiguous condition‎, ‎according to the findings‎.

It is very important to choose an appropriate and efficient monitoring system to evaluate the performance of a bank’s financial distress, including the most important monitoring systems that have been proposed to evaluate the performance of a bank’s financial distress; The use of CAMELS monitoring system, which includes six indicators of capital adequacy, asset quality, management soundness, earning quality, liquidity, market risk sensitivity, so the purpose of this study is to evaluate the financial distress of banks based on CAMELS indicators. In this regard, 12 financial variables based on CAMELS indices have been used, which have been implemented on 17 banks listed on the Tehran Stock Exchange. The sample selected in the model fit includes two groups of healthy and financially distressed, which are separated based on the CAMELS index. The accuracy of both models has been investigated. The results indicate that the overall accuracy of the logistic regression model is higher than the Data Envelopment Analysis model in assessing financial distress. Also, the results of this study showed that CAMELS financial ratios can be a good assessor for banks’ financial distress.

Mathematical modeling is one of the feasible methods that can be used to solve real problems. Modeling can be done using a variety of methods, including statistical methods that can be used to predict a variety of events. Health is one of the most important areas of research in the world today. Among the various diseases in the health sector, this study concerns stroke which is the second leading cause of death and long-term human disability, that has led to doing this research. The main objective of this research is to design and to build a predictive model of stroke based on symptoms and clinical reports, whether or not stroke occurs in patients in the near future. Using logistic regression technology, the main pathogenic factors of stroke have been found and their incidence has been predicted. In this study, clinical information from 5411 patients was collected and, after applying the LR method, the predictive model was designed.

Hepatitis-related liver diseases are a leading cause of mortality and morbidity among people with HIV/ AIDS taking highly active antiretroviral therapy due to shared transmission routes. An estimated 2–4 million HIV-infected persons have chronic HBV co-infection, and 4–5 million have HCV co-infection worldwide and 14,000 new infections each day. The purpose of this study was to determine the prevalence and associated factors of HBV and HCV co-infection in HIV-positive patients. A cross-sectional study was conducted among 235 HIV/ AIDS patients seeking medical care at special clinics of two public hospitals in Lahore, Pakistan, from February 2018 to May 2018. A structured questionnaire was used to collect information on socio-demographic and clinical characteristics of HIV/ AIDS patients after obtaining their written informed consent. Chi-square, Fisher's exact, and two independent sample t-tests as appropriate were used to find the association between risk factors and HBV, HCV co-infection with HIV. Further, a forward stepwise logistic regression model was used to evaluate the predictors of HBV and HCV co-infection with HIV. P-value < 0.05 was regarded as significant. Of 235 HIV-positive patients, 9\% were co-infected with HBV, 41 were HCV co-infected, and 6\% had HBV-HCV triple infection. The highest prevalence of HBV (55\%), HCV co-infection (70\%), and HBV-HCV triple infection (85\%) were observed in intravenous drug users followed by heterosexual routes. Male, hypertensive, alcohol consumers, and smokers were statistically significantly associated with HBV co-infection (P−value<0.05). The factors include being male, never married, having <1 year of HIV diagnosis, having <200 CD4 counts (cell/mm3), presence of physical disability, having been infected through sexual routes, injecting drug user, alcohol consumer, and smoker were statistically significantly associated with HCV co-infection (P−value<0.05). Whereas the factors; heterosexual transmission, intravenous drug use, alcohol use, smoking, and presence of physical disability were statistically significantly associated with HBV, HCV triple infection (P<0.05). The adjusted odds ratio obtained by fitted logistic regression model showed that HIV transmission routes (both hetero and homo) and never married had lesser odds of HCV co-infection whereas the person with HIV transmission through intravenous drug use, who smoke and aged more than 30 years, had greater odds of HCV co-infection. Co-infection with hepatitis B and C virus is common among this studied sample of HIV-infected patients. The study's finding reaffirms the need for routine baseline screening for this marker and as there is more chance of co-infection with these hepatitis viruses due to enhanced immunodeficiency by HIV and shared routes of transmission. It highlights the need for timely initiation of HAART. Furthermore, those found to be negative should be immunized with HBV and HCV vaccines to improve.

Logistic regression is commonly statistical methods used in empirical study including categorical dependent variables .In accurate study of reviews dental carries   associated with some variables as sex, drink milk, smoking, Extract teeth and diabetics. Aim of study is examine common practices of the results and interpretation of logistic regression data .Determinate some variables (dependent variables that was effect on denial carries. In results and by used two methods as enter methods and steps wise forward to determine sex and extract teeth which more dependent variables on independent variables (yi) dental carries.

A stroke occurs in the scenario wherein the blood supply to the brain is blocked, leading to a lack of oxygen to the blood. There is a need for the early diagnosis of the stroke to handle the emergency situations of stroke in an efficient manner. Integration of Artificial Intelligence (AI) in the early diagnosis of stroke provides efficiency and flexibility. Artificial Intelligence (AI), which is a mimic of human intelligence has a wide range of applications from small scale systems to high-end enterprise systems. Artificial Intelligence has emerged as an efficient and accurate decision-making system in healthcare systems. Machine Learning (ML) is a subset of Artificial Intelligence (AI). The incorporation of machine learning techniques in stroke diagnosis systems provides faster and precise decisions. The proposed system aims to develop an early diagnosis of stroke disorder using a homogenous logistic regression ensemble classifier. Logistic regression is a linear algorithm that uses maximum likelihood methodology for predictions and a standard machine learning model for two- class problems. The prediction is improved by accumulating the predictions of two or more logistic regression using a bagging ensemble classifier thereby increasing the accuracy of the stroke diagnosis system. The accumulation of prediction of two or more same models is known as a homogenous ensemble classifier. The results obtained show that the proposed homogenous logistic regression ensemble model has higher accuracy than single logistic regression.

Logistic regression is a non-linear modification of the linear regression. The purpose of the logistic regression analysis is to measure the effects of multiple explanatory variables which can be continuous and response variable is categorical. In real life there are situations which we deal with information that is vague in nature and there are cases that are not explained precisely. In this regard, we have used the concept of possiblistic odds and fuzzy approach. Fuzzy logic deals with linguistic uncertainties and extracting valuable information from linguistic terms. In our study, we have developed fuzzy possiblistic logistic model with trapezoidal membership function and fuzzy possiblistic logistic model is a tool that help us to deal with imprecise observations. Comparison fuzzy logistic regression model with classical logistic regression has been done by goodness of fit criteria on real life as an example.