• Childhood Kidney Diseases
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• v.10 no.2
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• pp.132-141
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• 2006

Purpose : This study was aimed to determine the predictive risk factors for the treatment response and relapse rate in children diagnosed with idiopathic nephrotic syndrome. Methods : We analyzed the medical records of children who were diagnosed and treated for childhood idiopathic nephrotic syndrome from November 1991 to May 2005. Variables selected in this study were age at onset, sex, laboratory data, concomitant bacterial infections, days to remission, and interval to first relapse. Results : There were 46 males and 11 females, giving a male:female ratio of 4.2:1. The age($mean{\pm}SD$) of patients was $5.8{\pm}4.1$ years old. Of all patients who were initially given corticosteroids, complete remission(CR) was observed in 54(94.7%). Of the 54 patients who showed CR with initial treatment, 40(70.2%) showed CR within 2 weeks and 14(24.6%) showed CR after 2 weeks. The levels of serum IgG were lower in the latter group who showed CR after 2 weeks(P=0.036). Of the 54 patients who showed CR with initial treatment, 47(82.5%) relapsed. Of these patients, 35.1% were frequent relapsers and 43.9% were infrequent relapsers. There was no significant correlation between the frequency of relapse and the following variables : sex, days to remission, and laboratory data. However, age at onset and interval to first relapse had a negative correlation with the frequency of relapse(Pearson's coefficient=-0.337, -0.433, P<0.012, P<0.01). Conclusion : The age at onset and the interval to first relapse were found to be predictive clinical parameters for the relapse rate, while the levels of serum IgG at initial presentation were a predictive laboratory factor for treatment response in childhood idiopathic nephrotic syndrome.

• Physical Therapy Rehabilitation Science
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• v.7 no.1
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• pp.29-34
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• 2018

Objective: This study aimed to predict balance and gait abilities with the Trunk Impairment scales (TIS) in persons with stroke. Design: Cross-sectional study. Methods: Sixty-eight participants with stoke were assessed with the TIS, Berg Balance scale (BBS), and Functional Gait Assessment (FGA) by a therapist. To describe of general characteristics, we used descriptive and frequency analyses, and the TIS was used as a predictive variable to determine the BBS. In the simple regression analysis, the TIS was used as a predictive variable for the BBS and FGA, and the TIS and BBS were used as predictive variables to determine the FGA in multiple regression analysis. Results: In the group with a BBS score of >45 for regression equation for predicting BBS score using TIS score, the coefficient of determination ($R^2$) was 0.234, and the $R^2$ was 0.500 in the group with a BBS score of ${\leq}45$. In the group with an FGA score >15 for regression equation for predicting FGA score using TIS score, the $R^2$ was 0.193, and regression equation for predicting FGA score using TIS score, the $R^2$ was 0.181 in the group of FGA score ${\leq}15$. In the group of FGA score >15 for regression equation for predicting FGA score using TIS and BBS score, the $R^2$ was 0.327. In the group of FGA score ${\leq}15$ for regression equation for predicting FGA score using TIS and BBS score, the $R^2$ was 0.316. Conclusions: The TIS scores are insufficient in predicting the FGA and BBS scores in those with higher balance ability, and the BBS and TIS could be used for predicting variables for FGA. However, TIS is a strong predictive variable for persons with stroke who have poor balance ability.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Research in Community and Public Health Nursing
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• v.17 no.1
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• pp.102-111
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• 2006

Purpose: This study was designed to test and develop a structural model that explains health promotion behaviors of elementary school students in Korea. Method: Data were collected using questionnaires from 329 6th-grade elementary school students in a city. The data were analyzed using LISREL 8.0 program. Result: Health promoting behaviors were directly affected by some of predictive factors particularly self-efficacy, self-esteem, perceived health status, importance of health and internal locus of control. These predictive variables of health promotion behaviors explained 67% of the total variance in the model. Life satisfaction was directly affected by self-efficacy, health promotion behaviors, self-esteem, importance of health, internal locus of control and perceived health status. Powerful other locus of control was identified as an important variable that contributed indirectly to the improvement of life satisfaction through enhancing health promoting behaviors. These predictive variables of life satisfaction explained 46% of the total variance in the model. Conclusion: The derived model in this study is considered appropriate in predicting health promotion behaviors and life satisfaction in elementary school students in Korea. Also it can be used effectively as a reference model for further study, and it is suggest that this study be used to set the direction of health promoting education.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.102-102
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• 2009

The purpose of this study is to establish the predictive equation of transversal residual stress at the thick weldment of large container ship. In order to do it, the variables used for this study were restraint degree, yield strength of base material, thickness of weldment and welding heat input. Here, the level of restraint degree at the thick weldment of container ship having the various welding sequence was calculated using FEA. From the result, the h-type specimen was designed to simulate the level of restraint degree at the actual weldment of containership. With H-type test specimen designed, the effect of the variables on the distribution of transversal residual stress at the weldment in a container ship was evaluated using the comprehensive FEA. Based on the results, the predictive equations of mean value and the distribution of transverse residual stress in each location of residual stress were established using dimensional analysis and multiple-regression method. The validation of predictive equations was verified by comparing with measured results by XRD in the actual weldment of the ship.

• The Journal of Korean Medicine
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• v.39 no.4
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• pp.16-29
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• 2018

Introduction: We aimed to investigate the predictive factors and optimal age for response to herbal medicine treatment for height gain in children. Methods: This retrospective chart review included 61 children (age range, 5-16 years) treated for height gain between 2011 and 2015. A predictive model was established by multiple linear regression analysis. Dependent variables were defined by the differences in percentile before and after herbal medicine treatment. The optimal cutoff value of patient age was determined by receiver operating curve analysis. Results : The age of initiation of herbal medicine therapy (p = 0.012) and administration of Forsythiae fructus (p = 0.002) were significant variables for treatment response. The adjusted R2 value was 0.231. The mean ages of the responder and non-responder groups were significantly different (p = 0.023). The optimal cutoff value of age for predicting treatment response was 9.75 years. Treatment response was better among children below 9.75 years of age. Conclusions: Patient age and administration of Forsythiae fructus were identified as determinants of response to herbal medicine treatment. Treatment of rhinitis and initiation of height gain treatment at an early age are critical for better response. These findings will provide fundamental data for further research.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.142-149
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• 1994

Substitution of graphic representation for extensive lists of numerical statistical data is highly desirable by both editors and readers of medical journals, faced with an exploding abundance of contemporary medical literature. A novel graphic tool. the 'double-ring diagram', is described herein which permits visual representation of information regarding certain statistical variables used to describe the performance of a test or physical sign in the diagnosis of a disease. The diagram is relatively easy to construct on the basis of a number of primary data such as the prevalence and the true positive, true negative. false positive and false negative test results. These values are reflected in the diagram along with the values of other statistical variables derived from them. such as the sensitivity. specificity, predictive values for positive and negative test result. and accuracy. This diagram may be useful in visualizing a test's performance and facilitating visual comparison of performance of two or more tests.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.05a
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• pp.75-78
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• 2008

The predictive equation of void-closure was developed to evaluate void crush ratio with respect to the process variables in the cogging process of a large round bar. The comprehensive finite element analysis with the process variables such as reduction ratio and die width ratio was carried out. The predictive equation of void-closure for cogging process was established on the basis of the regression analysis with the extensive FE analysis results and verified by comparing the predicted results with FEA results with various forging passes.