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  • Title/Summary/Keyword: LOGISTIC REGRESSION ANALYSIS

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Relationship between smoking history and periodontal disease among the elderly in Korea

  • Kim, So-Yeong
    • Journal of Korean society of Dental Hygiene
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    • v.21 no.3
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    • pp.227-234
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    • 2021
  • Objectives: The objective of this study was to investigate the relationship between smoking history and periodontal disease among the elderly in South Korea. Methods: The study subjects comprised 2,703 elderly people who underwent oral health examination as part of the 7th South Korea National Health and Nutrition Examination Survey (KNHANES; 2016-2018). Data were analyzed using frequency analysis, Rao-Scott chi-square test, t-test, and binary logistic regression analysis. Results: A complex sample logistic regression analysis showed that the odds for periodontal disease development were higher in past smokers (odds ratio [OR]=1.461; 95% CI=1.070-1.994) and current smokers (OR=1.601; 95% CI=1.011-2.536) than in lifetime non-smokers. Conclusions: Smokers must actively participate in smoking cessation programs and interventions starting from middle age.

Determining the complexity level of proceduralized tasks in a digitalized main control room using the TACOM measure

  • Inseok Jang;Jinkyun Park
    • Nuclear Engineering and Technology
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    • v.54 no.11
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    • pp.4170-4180
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    • 2022
  • The task complexity (TACOM) measure was previously developed to quantify the complexity of proceduralized tasks conducted by nuclear power plant operators. Following the development of the TACOM measure, its appropriateness has been validated by investigating the relationship between TACOM scores and three kinds of human performance data, namely response times, human error probabilities, and subjective workload scores. However, the information reflected in quantified TACOM scores is still insufficient to determine the levels of complexity of proceduralized tasks for human reliability analysis (HRA) applications. In this regard, the objective of this study is to suggest criteria for determining the levels of task complexity based on logistic regression between human error occurrences in digitalized main control rooms and TACOM scores. Analysis results confirmed that the likelihood of human error occurrence according to the TACOM score is secured. This result strongly implies that the TACOM measure can be used to identify the levels of task complexity, which could be applicable to various research domains including HRA.

Factors Influencing Supercomputing Resource Selection with PCA

  • Hyungwook Shim;Myungju Ko;Sunyoung Hwang;Jaegyoon Hahm
    • Asian Journal of Innovation and Policy
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    • v.13 no.1
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    • pp.57-67
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    • 2024
  • This paper analyzes the factors influencing the selection of supercomputing resources. Using the results of a survey targeting supercomputing resources in the public sector, a resource selection model was presented through logistic regression and principal component analysis methods. As a result of the analysis, it was confirmed that affiliation, purpose of use, size of research funding, possession of a supercomputer, and whether specialized services are needed have a significant impact on resource selection. In the future, we expect that the results of this study will be used in various ways to manage demand for supercomputing resources.

A Statistical Mobilization Criterion for Debris-flow (통계 분석을 통한 산사태 토석류 전이규준 모델)

  • Yoon, Seok;Lee, Seung-Rae;Kang, Sin-Hang;Park, Do-Won
    • Journal of the Korean Geotechnical Society
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    • v.31 no.6
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    • pp.59-69
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    • 2015
  • Recently, landslide and debris-flow disasters caused by severe rain storms have frequently occurred. Many researches related to landslide susceptibility analysis and debris-flow hazard analysis have been conducted, but there are not many researches related to mobilization analysis for landslides transforming into debris-flow in slope areas. In this study, statistical analyses such as discriminant analysis and logistic regression analysis were conducted to develop a mobilization criterion using geomorphological and geological factors. Ten parameters of geomorphological and geological factors were used as independent variables, and 466 cases (228 non-mobilization cases and 238 mobilization cases) were investigated for the statistical analyses. First of all, Fisher's discriminant function was used for the mobilization criterion. It showed 91.6 percent in the accuracy of actual mobilization cases, but homogeneity condition of variance and covariance between non-mobilization and mobilization groups was not satisfied, and independent variables did not follow normal distribution, either. Second, binomial logistic analysis was conducted for the mobilization criterion. The result showed 92.3 percent in the accuracy of actual mobilization cases, and all assumptions for the logistic analysis were satisfied. Therefore, it can be concluded that the mobilization criterion for debris-flow using binomial logistic regression analysis can be effectively applied for the prediction of debris-flow hazard analysis.

A Study to Validate the Pretest Probability of Malignancy in Solitary Pulmonary Nodule (사전검사를 통한 고립성 폐결절 환자에서의 악성 확률 타당성에 대한 연구)

  • Jang, Joo Hyun;Park, Sung Hoon;Choi, Jeong Hee;Lee, Chang Youl;Hwang, Yong Il;Shin, Tae Rim;Park, Yong Bum;Lee, Jae Young;Jang, Seung Hun;Kim, Cheol Hong;Park, Sang Myeon;Kim, Dong Gyu;Lee, Myung Goo;Hyun, In Gyu;Jung, Ki Suck
    • Tuberculosis and Respiratory Diseases
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    • v.67 no.2
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    • pp.105-112
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    • 2009
  • Background: Solitary pulmonary nodules (SPN) are encountered incidentally in 0.2% of patients who undergo chest X-ray or chest CT. Although SPN has malignant potential, it cannot be treated surgically by biopsy in all patients. The first stage is to determine if patients with SPN require periodic observation and biopsy or resection. An important early step in the management of patients with SPN is to estimate the clinical pretest probability of a malignancy. In every patient with SPN, it is recommended that clinicians estimate the pretest probability of a malignancy either qualitatively using clinical judgment or quantitatively using a validated model. This study examined whether Bayesian analysis or multiple logistic regression analysis is more predictive of the probability of a malignancy in SPN. Methods: From January 2005 to December 2008, this study enrolled 63 participants with SPN at the Kangnam Sacred Hospital. The accuracy of Bayesian analysis and Bayesian analysis with a FDG-PET scan, and Multiple logistic regression analysis was compared retrospectively. The accurate probability of a malignancy in a patient was compared by taking the chest CT and pathology of SPN patients with <30 mm at CXR incidentally. Results: From those participated in study, 27 people (42.9%) were classified as having a malignancy, and 36 people were benign. The result of the malignant estimation by Bayesian analysis was 0.779 (95% confidence interval [CI], 0.657 to 0.874). Using Multiple logistic regression analysis, the result was 0.684 (95% CI, 0.555 to 0.796). This suggests that Bayesian analysis provides a more accurate examination than multiple logistic regression analysis. Conclusion: Bayesian analysis is better than multiple logistic regression analysis in predicting the probability of a malignancy in solitary pulmonary nodules but the difference was not statistically significant.

Study on Revitalizing Commercial Freight Vehicles Using Freight Transport Mode Selection (화물운송수단선택모형을 이용한 영업용화물차량 이용 활성화 방안 연구)

  • Kim, Min-Young;Kang, Kyung-Woo
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.6 no.2
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    • pp.57-69
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    • 2007
  • The most important problem in logistic activities may be to decrease the transportation efficiency due to the traffic congestion in urban areas. The traffic congestion reduces the average travel speed of freight vehicles, and then increases the travel time. These problems can lead the logistic system to be inefficient. As a result, it causes an increase of transportation costs. In addition, the increased cost is a main barrier for the transition to an advanced logistic system. This study focuses on the analysis of key factors choosing commercial freight vehicles using Logistic regression-analysis with RP (Revealed Preference) data to solve the increase of private freight cars and transportation costs. Additionally, this paper presents policies to promote good use of commercial freight vehicles based on the results of this study.

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Development for City Bus Dirver's Accident Occurrence Prediction Model Based on Digital Tachometer Records (디지털 운행기록에 근거한 시내버스 운전자의 사고발생 예측모형 개발)

  • Kim, Jung-yeul;Kum, Ki-jung
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.15 no.1
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    • pp.1-15
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    • 2016
  • This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration (Xdeceleration) and acceleration to the right (Yright) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

Statistical Analysis for Risk Factors and Prediction of Hypertension based on Health Behavior Information (건강행위정보기반 고혈압 위험인자 및 예측을 위한 통계분석)

  • Heo, Byeong Mun;Kim, Sang Yeob;Ryu, Keun Ho
    • Journal of Digital Contents Society
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    • v.19 no.4
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    • pp.685-692
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    • 2018
  • The purpose of this study is to develop a prediction model of hypertension in middle-aged adults using Statistical analysis. Statistical analysis and prediction models were developed using the National Health and Nutrition Survey (2013-2016).Binary logistic regression analysis showed statistically significant risk factors for hypertension, and a predictive model was developed using logistic regression and the Naive Bayes algorithm using Wrapper approach technique. In the statistical analysis, WHtR(p<0.0001, OR = 2.0242) in men and AGE (p<0.0001, OR = 3.9185) in women were the most related factors to hypertension. In the performance evaluation of the prediction model, the logistic regression model showed the best predictive power in men (AUC = 0.782) and women (AUC = 0.858). Our findings provide important information for developing large-scale screening tools for hypertension and can be used as the basis for hypertension research.

Analysis of Korean Adolescents' Life Satisfaction based on Public Database and Data Mining Techniques: Emphasis on Decision Tree (공공 DB 데이터마이닝 기법을 활용한 국내 청소년 삶의 만족도 분석에 관한 실증연구: 의사결정나무 기법을 중심으로)

  • Jo, Hyun Jin;Ko, Geo Nu;Lee, Kun Chang
    • Journal of Digital Convergence
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    • v.18 no.6
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    • pp.297-309
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    • 2020
  • This study focuses on the application of the data mining technique logistic regression analysis and decision tree analysis to the domestic public database called Korean Children Youth Panel Survey (KCYPS) to derive a series of important factors affecting the enhancement of life satisfaction of domestic youth. As a result, the general impact factors on life satisfaction for each grade were derived from logistic regression. Using decision tree analysis, we came to conclusions that those factors such as depression, overall grade satisfaction, household economic level, and school adaptation play crucial roles in affecting high school adolesscents' life satisfaction.

The Landslide Probability Analysis using Logistic Regression Analysis and Artificial Neural Network Methods in Jeju (로지스틱회귀분석기법과 인공신경망기법을 이용한 제주지역 산사태가능성분석)

  • Quan, He Chun;Lee, Byung-Gul;Lee, Chang-Sun;Ko, Jung-Woo
    • Journal of Korean Society for Geospatial Information Science
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    • v.19 no.3
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    • pp.33-40
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    • 2011
  • This paper presents the prediction and evaluation of landslide using LRA(logistic regression analysis) and ANN (Artificial Neural Network) methods. In order to assess the landslide, we selected Sarabong, Byeoldobong area and Mt. Song-ak in Jeju Island. Five factors which affect the landslide were selected as: slope angle, elevation, porosity, dry density, permeability. So as to predict and evaluate the landslide, firstly the weight value of each factor was analyzed by LRA(logistic regression analysis) and ANN(Artificial Neural Network) methods. Then we got two prediction maps using AcrView software through GIS(Geographic Information System) method. The comparative analysis reveals that the slope angle and porosity play important roles in landslide. Prediction map generated by LRA method is more accurate than ANN method in Jeju. From the prediction map, we found that the most dangerous area is distributed around the road and path.