• The Journal of Korean Society for School & Community Health Education
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• v.22 no.3
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• pp.83-96
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• 2021

Background & objectives: Cognitive function decline is a main factor influencing the overall life of the elderly and places a burden of society. The aime of this study was to investigate the risk factors of cognitive function decline of elderly living alone and living with a spouse. Methods: This study used the Korean Longitudinal Study of Ageing from 2014 to 2018. 243 older adults who lived alone and 1,155 lived with a spouse with the Korean version of Mini Mental State Examination scores in normal range at the time of 2014 were included in the analysis. Logistic regression analysis was conducted to determine the difference of risk factors affecting cognitive function decline between in elderly living alone and elderly living with a spouse. Results: Cognitive function decline incidence rate of elderly living alone was 30.5% and the elderly living with a spouse showed 23.0%. According to the results of logistic regression analysis, the risk factors of cognitive function decline in the elderly living alone was age and residential area, while in the elderly living with a spouse were age, education level, social networks, and depression. Conclusions: The factors that affect the cognitive function decline of the elderly living alone and the elderly living with a spouse were different. Accordingly, other measures to prevent cognitive decline are necessary.

• Tuberculosis and Respiratory Diseases
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• v.87 no.3
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• pp.357-367
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• 2024

Background: Obstructive sleep apnea (OSA) is a prevalent sleep disorder associated with various health issues. Although some studies have suggested an association between reduced lung function and OSA, this association remains unclear. Our study aimed to explore this relationship using data from a nationally representative population-based survey. Methods: We performed an analysis of data from the 2019 Korea National Health and Nutrition Examination Survey. Our study encompassed 3,675 participants aged 40 years and older. Risk of OSA was assessed using the STOP-Bang (Snoring, Tiredness during daytime, Observed apnea, and high blood Pressure-Body mass index, Age, Neck circumference, Gender) questionnaire and lung function tests were performed using a portable spirometer. Logistic regression analysis was applied to identify the risk factors associated with a high-risk of OSA, defined as a STOP-Bang score of ≥3. Results: Of 3,675 participants, 600 (16.3%) were classified into high-risk OSA group. Participants in the high-risk OSA group were older, had a higher body mass index, and a higher proportion of males and ever-smokers. They also reported lower lung function and quality of life index in various domains along with increased respiratory symptoms. Univariate logistic regression analysis indicated a significant association between impaired lung function and a high-risk of OSA. However, in the multivariable analysis, only chronic cough (odds ratio [OR], 2.413; 95% confidence interval [CI], 1.383 to 4.213) and sputum production (OR, 1.868; 95% CI, 1.166 to 2.992) remained significantly associated with a high OSA risk. Conclusion: Our study suggested that, rather than baseline lung function, chronic cough, and sputum production are more significantly associated with OSA risk.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Transactions of Materials Processing
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• v.26 no.1
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• pp.18-27
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• 2017

A comparative study was performed on strain-compensated Arrhenius-type constitutive models established with two regression methods: polynomial regression and regression Kriging. For measurements at high temperatures, experimental data of 70Cr3Mo steel were adopted from previous research. An Arrhenius-type constitutive model necessitates strain compensation for material constants to account for strain effect. To associate the material constants with strain, we first evaluated them at a set of discrete strains, then capitalized on surrogate modeling to represent the material constants as a function of strain. As a result, disparate flow stress models were formed via the two different regression methods. The constructed constitutive models were examined systematically against measured flow stresses by validation methods. The predicted material constants were found to be quite accurate compared to the actual material constants. However, notable mismatches between measured and predicted flow stresses were revealed by the proposed validation techniques, which carry out validation with not the entire, but a single tensile test case.

• Journal of Families and Better Life
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• v.32 no.1
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• pp.13-26
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• 2014

The purpose of this study was to explore the effects of parenting behaviors on preschoolers' executive function, focusing on methods of measuring executive function. The subjects of this study were 166 preschoolers who were 3 to 5 years of age, and their parents. Data were collected by various performance-based tests and their parents' reports and analyzed by descriptive statistics and hierarchical linear regression analysis using the SPSS 19.0 program. The major results were as follows: First, maternal autonomous and paternal affective parenting behaviors significantly affected preschoolers' performance-based executive function. Second, maternal affective parenting behaviors significantly affected preschoolers' parent-report executive function. The results suggest the importance of positive parenting practices in the development of preschoolers' executive function.

• Journal of Fisheries and Marine Sciences Education
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• v.21 no.2
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• pp.278-290
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• 2009

The purpose of this study is to investigate the relationship between learning skills and cognitive functions on elementary school students. In this study CAS and Learning Skills Test(LST) were administered with 3 to 6 grade, 60 students from 5 elementary schools. The data were analyzed according to Pearson's correlation and Stepwise Multiple Regression Analysis. The results are as follows. Firstly, girls and older students showed significantly higher ability than boys and younger students on the learning skills. And girls significantly outperformed boys on the planning function and attention function and on the simultaneous cognitive function was the other way round. Secondly, learning skills were explained 41% by two variables that the planning function and the successive function which are sub factors of the cognitive function. And then, planning and successive processing effected to self-management, attention and planning to test-taking skills, successive processing and attention to class-participation skills, and successive processing to information processing.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.589-597
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• 2021

In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1483-1490
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• 2004

To accomplish a project successfuly, we have to estimate develpment effort accurately. But, development effort is different to software size and operation environment. Usually, we made use of function point for estimating development effort. In this paper. we make use of 789 project data. It is related to development projects in 1990`s. We investigate the variable affecting development effort. Also, we exedcute multiple liner regression analysis for looking linear relation about variables. We find the regression equation for multistage by dividing PDR that influ-enced development effort step by step.

• Earthquakes and Structures
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• v.14 no.3
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• pp.203-214
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• 2018

Predictive demand and collapse fragility functions are two essential components of the probabilistic seismic demand analysis that are commonly developed based on statistics with enormous, costly and time consuming data gathering. Although this approach might be justified for research purposes, it is not appealing for practical applications because of its computational cost. Thus, in this paper, Bayesian regression-based demand and collapse models are proposed to eliminate the need of time-consuming analyses. The demand model developed in the form of linear equation predicts overall maximum inter-story drift of the lowto mid-rise regular steel moment resisting frames (SMRFs), while the collapse model mathematically expressed by lognormal cumulative distribution function provides collapse occurrence probability for a given spectral acceleration at the fundamental period of the structure. Next, as an application, the proposed demand and collapse functions are implemented in a seismic fragility analysis to develop fragility and consequently seismic demand curves of three example buildings. The accuracy provided by utilization of the proposed models, with considering computation reduction, are compared with those directly obtained from Incremental Dynamic analysis, which is a computer-intensive procedure.

• Journal of Biosystems Engineering
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• v.31 no.3 s.116
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• pp.194-202
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• 2006

Using a multi-variable linear regression technique and SUR(seemingly unrelated regression) model, the demand functions of tractor, combine and rice transplanter were estimated. The demand was regarded as an annual supply of each machine and modeled as a function of 11 independent variables which reflect the actual farmer's income, actual prices of farm machines, previous supply, previous stock, actual amount of available subsidy, actual amount of available loan, arable land, import of farm machines and rice price. The actual amount of available loan affects most significantly the demand functions. The actual farmer's income, actual farmer's asset, loan coverage, and rice price affect the demand positively while prices of farm machines and import negatively. The annual demands of tractor, combine and rice transplanter estimated using the demand functions were also presented over the next 4 years.