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  • Title/Summary/Keyword: LOGISTIC REGRESSION ANALYSIS

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Identification of risk factors and development of the nomogram for delirium

  • Shin, Min-Seok;Jang, Ji-Eun;Lee, Jea-Young
    • Communications for Statistical Applications and Methods
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    • v.28 no.4
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    • pp.339-350
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    • 2021
  • In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.

Factors related to undiagnosed diabetes in Korean adults: a secondary data analysis (한국 성인의 당뇨병 미진단 비율 영향요인: 2차 자료 분석 연구)

  • Bohyun Kim
    • Journal of Korean Biological Nursing Science
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    • v.25 no.4
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    • pp.295-305
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    • 2023
  • Purpose: This study compared health behaviors and health-related clinical characteristics between individuals with normal glucose levels without diabetes and those with undiagnosed diabetes. Factors that were associated with undiagnosed diabetes were identified by sex. Methods: This was an observational study with a cross-sectional design based on data from the eighth Korea National Health and Nutrition Examination Survey, which used a stratified, multi-stage, cluster-sampling design to obtain a nationally representative sample. Multiple logistic regression analysis was employed to compute the odds ratios of health behaviors and clinical characteristics to identify risk factors for undiagnosed diabetes. Results: The overall prevalence of undiagnosed diabetes was 5.2% (weighted %, n = 700, p < .001). Among individuals with undiagnosed diabetes, 58.3% were men. Univariate logistic regression for undiagnosed diabetes identified sex, age, house income, educational level, and triglycerides as influencing factors. In multiple logistic regression by sex, the factors associated with undiagnosed diabetes in men were age, perceived health status, a diagnosis of angina, and triglycerides. Conclusion: Strategies should be targeted to improve health behaviors and clinical characteristics for specific age groups, men in bad perceived health status, women with high systolic blood pressure, and high triglycerides. Moreover, healthcare providers should understand the barriers to health behaviors and health-related quality of life to effectively deliver healthcare services.

Which Alarm Symptoms Are Associated With Abnormal Gastrointestinal Endoscopy Among Thai Children?

  • Anundorn Wongteerasut
    • Pediatric Gastroenterology, Hepatology & Nutrition
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    • v.27 no.2
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    • pp.113-124
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    • 2024
  • Purpose: Alarm symptoms (red flag signs) are crucial indications for management decisions on pediatric gastrointestinal endoscopy. We aimed to identify items in the alarm symptoms and pre-endoscopic investigations that predict abnormal endoscopy results. Methods: A retrospective descriptive study was conducted among children aged under 18 years undergoing endoscopy. The patients were classified into normal and abnormal endoscopic groups. The incidence of alarm symptoms and pre-endoscopic investigations were compared between the groups. Univariate and multivariate logistic regression analyses were performed to determine independent risk factors for abnormal endoscopy. Results: Of 148 participants, 66 were classified in the abnormal endoscopy group. Compared with the normal group, the abnormal group had a significantly higher prevalence of alarm symptoms. Moreover, hematemesis/hematochezia, anemia, low hemoglobin level, hypoalbuminemia, rising erythrocyte sedimentation rate, increased serum lipase, and blood urea nitrogen/creatinine ratio were significantly higher in the abnormal endoscopy group than in the normal group. Multivariate logistic regression analysis indicated that hematemesis/hematochezia and low hemoglobin level were independent risk factors for abnormal endoscopy. Conclusion: The alarm symptoms and pre-endoscopic investigations were evaluated using predictive factors for abnormal pediatric endoscopic findings. According to multivariate logistic regression analysis, hematemesis/hematochezia and low hemoglobin levels were independent risk factors for abnormal endoscopy.

A Statistical Analysis of Professional Baseball Team Data: The Case of the Lotte Giants

  • Cho, Young-Seuk;Han, Jun-Tae;Park, Chan-Keun;Heo, Tae-Young
    • The Korean Journal of Applied Statistics
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    • v.23 no.6
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    • pp.1191-1199
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    • 2010
  • Knowing what factors into a player's ability to affect the outcome of a sports game is crucial. This knowledge helps determine the relative degree of contribution by each team member as well as sets appropriate annual salaries. This study uses statistical analysis to investigate how much the outcome of a professional baseball game is influenced by the records of individual players. We used the Lotte Giants' data on 252 games played between 2007 and 2008 that included environmental data(home or away games and opponents) as well as pitchers' and batters' data. Using a SAS Enterprise Miner, we performed a logistic regression analysis and decision tree analysis on the data. The results obtained through the two analytic methods are compared and discussed.

An Improved Technology Appraisal Model Considering Macroeconomic Variable : A Case of KOTEC (거시경제변수를 고려한 기술평가모형의 개선 : 기술보증기금의 사례)

  • Kim, Dae Cheol;Kim, Jae Bum;Cho, Keun Tae
    • Korean Management Science Review
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    • v.30 no.2
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    • pp.117-132
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    • 2013
  • The objective of this paper is to provide an improved technology appraisal model, which considers a variety of macroeconomic variables such as consumer price index and producer price index. The improved model was built using cross correlation analysis and logistic regression analysis. The AUROC analysis showed that goodness-of-fit of the proposed model turned out to be improved than that of the existing model. The model proposed in the paper would be helpful for making a reasonable investments and financing decision, lessening the default rates by systematic risk management, and enhancing the technology commercialization capabilities.

Two-stage imputation method to handle missing data for categorical response variable

  • Jong-Min Kim;Kee-Jae Lee;Seung-Joo Lee
    • Communications for Statistical Applications and Methods
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    • v.30 no.6
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    • pp.577-587
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    • 2023
  • Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

Development of a Logistic Regression Model for Analyzing Site Characteristics of Tombs Surrounding Expressway in Aerial Photographs (항공사진에 나타난 고속국도 주변 묘지의 입지 분석을 위한 로지스틱 회귀모형의 개발)

  • Han, Hee;Seol, A-Ra;Chung, JooSang
    • Journal of the Korean Association of Geographic Information Studies
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    • v.11 no.4
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    • pp.193-202
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    • 2008
  • The objectives of this study are to analyze the spatial site characteristics of existing tombs and the change in the pattern of spatial distributions of tombs over time. The spatial distributions of tombs located in Honam province along the Honam expressway were investigated by interpreting digital aerial photographs taken in two different points of time; 1990 and 2000. According to the results of the study, the tombs newly observed in 2000 photos were located closer to roads and villages than those found in the photos of 1990. This is a finding indicating that the accessibility of tombs has been more important consideration in determining the location of tomb sites. Also found were the gentle slopes of southern aspects to be favored as tomb sites. Based on the data sets of tombs locations and their topographic site characteristics, the probability function of tombs appearance in the study area was derived using the logistic regression analysis technique. As a result, tomb sites were classified as 74.7% by logistic regression. All of six input factors (elevation, slope, aspect, distance from the roads, the town and the stream, respectively) affected the probability of tombs appearance significantly.

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Monocyte Count and Systemic Immune-Inflammation Index Score as Predictors of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage

  • Yeonhu Lee;Yong Cheol Lim
    • Journal of Korean Neurosurgical Society
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    • v.67 no.2
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    • pp.177-185
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    • 2024
  • Objective : Delayed cerebral ischemia (DCI) is a major cause of disability in patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammatory markers, such as peripheral leukocyte count and systemic immune-inflammatory index (SII) score, have been considered predictors of DCI in previous studies. This study aims to investigate which systemic biomarkers are significant predictors of DCI. Methods : We conducted a retrospective, observational, single-center study of 170 patients with SAH admitted between May 2018 and March 2022. We analyzed the patients' clinical and laboratory parameters within 1 hour and 3-4 and 5-7 days after admission. The DCI and non-DCI groups were compared. Variables showing statistical significance in the univariate logistic analysis (p<0.05) were entered into a multivariate regression model. Results : Hunt-Hess grade "4-5" at admission, modified Fisher scale grade "3-4" at admission, hydrocephalus, intraventricular hemorrhage, and infection showed statistical significance (p<0.05) on a univariate logistic regression. Lymphocyte and monocyte count at admission, SII scores and C-reactive protein levels on days 3-4, and leukocyte and neutrophil counts on days 5-7 exhibited statistical significance on the univariate logistic regression. Multivariate logistic regression analysis revealed that monocyte count at admission (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.65; p=0.036) and SII score at days 3-4 (OR, 1.55; 95% CI, 1.02-2.47; p=0.049) were independent predictors of DCI. Conclusion : Monocyte count at admission and SII score 3-4 days after rupture are independent predictors of clinical deterioration caused by DCI after aSAH. Peripheral monocytosis may be the primer for the innate immune reaction, and the SII score at days 3-4 can promptly represent the propagated systemic immune reaction toward DCI.

Analysis of the Effects of Population, Household, and Housing Characteristics on the Status of Empty Houses Using Population Housing Census Data (인구주택 총조사 자료를 이용한 인구, 가구, 주택 특성과 빈집 현황 분석)

  • Lee, Jimin;Choi, Won
    • Journal of The Korean Society of Agricultural Engineers
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    • v.62 no.5
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    • pp.1-13
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    • 2020
  • The empty houses' problem is important in the local revitalization and local sustainability, and these phenomenon caused by various factors of the region. The population and housing census data are the most effective data available to study this phenomenon by small regions. In this study, logistic regression and multiple regression analysis were performed to understand the effects of population, household, and housing characteristics on empty houses using population and housing census data. Also, the scale and direction of the effect of each characteristic in large cities, small cities, and rural areas were compared. As results, there was a slight difference between cities and province regions in the district and housing characteristic variables. In the comparison of Eup-Myeon-Dong, the affected variables were different in the Dong and Myeon areas. The significance of this study is to examine the effect of the characteristics of population and housing on the vacant houses and to confirm that the factors affecting different regions.

A Prediction Model of Landslides in the Tertiary Sedimentary Rocks and Volcanic Rocks Area (제3기 퇴적암 및 화산암 분포지의 산사태 예측모델)

  • Chae Byung-Gon;Kim Won-Young;Na Jong-Hwa;Cho Yong-Chan;Kim Kyeong-Su;Lee Choon-Oh
    • The Journal of Engineering Geology
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    • v.14 no.4 s.41
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    • pp.443-450
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    • 2004
  • This study developed a prediction model of debris flow to predict a landslide probability on natural terrain composed of the Tertiary sedimentary and volcanic rocks using a logistic regression analysis. The landslides data were collected around Pohang, Gyeongbuk province where more than 100 landslides were occurred in 1998. Considered with basic characteristics of the logistic regression analysis, field survey and laboratory soil tests were performed for both slided points and not-slided points. The final iufluential factors on landslides were selected as six factors by the logistic regression analysis. The six factors are composed of two topographic factors and four geologic factors. The developed landslide prediction model has more than 90% of prediction accuracy. Therefore, it is possible to make probabilistic and quantitative prediction of landslide occurrence using the developed model in this study area as well as the previously developed model for metamorphic and granitic rocks.