An Application of Logistic Regression Modeling to Predict Risk Factors for Bypass Graft Diagnosis in Erbil

Khudhur, Azhin M.; Kadir, Dler H.

doi:10.24086/cuesj.v6n1y2022.pp57-63

Cited by 6 publications

(1 citation statement)

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“…This difference among logistic and linear regression is reflected both in the choice of a parametric model and it assumptions, whereas the methods used in a logistic, regression study follow the same fundamental principles, as linear regression. [16,17] Logistic regression model circuitously models the response variable created on probabilities linked with the digits of the dependent variable y. We will use P(X) to represent the possibility of a response when y = 1.…”

Section: Logistic Regression Modelmentioning

confidence: 99%

The Use of Tobit and Logistic Regression Models to Study Factors that Affect Blood Pressure in Cardiac Patients

Meran

Sedeeq

2022

Cihan U Erbil SCI J

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This research witnessed using the Tobit and logistic regression models to study the factors that affect blood pressure in cardiac patients. The data have been collected, from 500 patients with heart disease in hospital-heart center - Erbil. The two levels of blood pressure, low and high blood pressures, were taken from the patients, as dependent variables and some, independent variables (Gender, Age, Urea, Cholesterol, Creatinine, and Weight). The researcher found that the median of blood pressure by means of arterial pressure (MAP) equation contains each of high and low blood pressures differently because the threshold point was determined to be (99.33), which is equal to,12/8 mmHg, which is the normal amount of blood pressure. The aim of this research is to explain the main concepts and processes of Tobit regression analysis (Censored- and Truncated) and logistic regression analysis, which is used for knowing which factors of independent variables has more effect on the response variables (blood pressure), and to compare the outcomes of the two models (Tobit regression and logistic regression) in order to determine which of the models best fits our data in which AIC and BIC are used. The researcher reached a conclusion that the logistic regression model best fits our data compared with Tobit regression, and it arrived at the results obtained by utilizing the statistical packages in R programming, MATLAB and Statistical Packages of Social Sciences (SPSS) V.26, which was used to analyze data from our research

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Section: Logistic Regression Modelmentioning

confidence: 99%

The Use of Tobit and Logistic Regression Models to Study Factors that Affect Blood Pressure in Cardiac Patients

Meran

Sedeeq

2022

Cihan U Erbil SCI J

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Assessing prevalence, factors and health consequences and academic performance of undergraduate students with breakfast skipping during COVID-19 using statistical modeling: a cross-sectional study

Kadir,

Saleem,

Galali

et al. 2024

Eat Weight Disord

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The study was conducted in order to study breakfast skipping (BKS) frequency, factors associated with, health consequence and undergraduate students academic performance during Covid-19 pandemic as earliest studies focusing on this area. A cross-sectional study was carried out among 2225 of undergraduate students. The study was carried between the period of 15/1/2020 to 3/4/2020 using an online self-report Breakfast Eating Habit Survey (BEHS). The BEHS survey was divided into two sections. The first sections included sociodemographic information (gender, BMI, age, smoking, residency, parental education, family income, studying system and stage (public or private), and studying institution (university or institute) academic performance. The second part included questions regarding breakfast eating habits including frequency of skipping meals, factors related to BKS health consequences and types of snacks. Logistic regression is a common technique used for modeling outcomes that fall into the range of 1 and 0. For this purpose, a logistic regression was performed to find adjusted odds ratio and crude odds ratio. The results showed that the majority of participants were female (1238, 55.7%). Out of 2,224 students, 2059 are aged between 18 to 24 years. Most of the participants were from first level (26.5%), second level (32.8%), third level (17.6%) or the fourth level (21.3%). Over 92% of participants were single and about 68% came from families of medium income families. The statistical analysis showed that the odds of BKS is reduced among students who live in accommodation by 54% (odds ratio = 54%, CI (41–71%), p value = 0.000). It seems that students with low income and normal or higher BMI are more likely to skip breakfast more regularly. The odds of skipping breakfast among students with BMI of 18–24.9 is reduced by 41% (odds ratio = 59%, CI (27%-93%), p value = 0.027) and the odds of BKS is reduced among students with BMI of 25–29.9 by 45% (odds ratio = 55%, CI (31–95%). Additionally, students with medium or high incomes are more likely to skip breakfast as much as twofold in comparison with students with low income (medium income (odds ratio = 1.85, CI (1.08–3.17), p-value = 0.024), high income (odds ratio = 1.98, CI (1.12–3.51), p-value = 0.019). The most common reasons for skipping breakfast included include time constraint, not hungry, breakfast is not ready, afraid to be overweight and lack of appetite. The consequences of skipping breakfast were feeling hungry throughout the day, feeling tired, and not paying attention in class and low academic performance. To concluded, BKS during Covid-19 is more common among students with higher BMI, higher income and living in accommodation. The main reason is time constraint and the most common health problems are being tired and luck of attention.

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Computational investigation of double non-uniform magnetic field on heat transfer of nanofluid stream along 180-degree elbow pipe

Zhu,

Wang,

Hassanvand

et al. 2023

Case Studies in Thermal Engineering

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An Application of Logistic Regression Modeling to Predict Risk Factors for Bypass Graft Diagnosis in Erbil

Cited by 6 publications

References 0 publications

The Use of Tobit and Logistic Regression Models to Study Factors that Affect Blood Pressure in Cardiac Patients

The Use of Tobit and Logistic Regression Models to Study Factors that Affect Blood Pressure in Cardiac Patients

Assessing prevalence, factors and health consequences and academic performance of undergraduate students with breakfast skipping during COVID-19 using statistical modeling: a cross-sectional study

Computational investigation of double non-uniform magnetic field on heat transfer of nanofluid stream along 180-degree elbow pipe

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