• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6181-6185
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• 2012

Background: The aim of the study was to evaluate the available breast nomograms (MSKCC, Stanford, Tenon) to predict non-sentinel lymph node metastasis (NSLNM) and to determine variables for NSLNM in SLN positive breast cancer patients in our population. Materials and Methods: We retrospectively reviewed 170 patients who underwent completion axillary lymph node dissection between Jul 2008 and Aug 2010 in our hospital. We validated three nomograms (MSKCC, Stanford, Tenon). The likelihood of having positive NSLNM based on various factors was evaluated by use of univariate analysis. Stepwise multivariate analysis was applied to estimate a predictive model for NSLNM. Four factors were found to contribute significantly to the logistic regression model, allowing design of a new formula to predict non-sentinel lymph node metastasis. The AUCs of the ROCs were used to describe the performance of the diagnostic value of MSKCC, Stanford, Tenon nomograms and our new nomogram. Results: After stepwise multiple logistic regression analysis, multifocality, proportion of positive SLN to total SLN, LVI, SLN extracapsular extention were found to be statistically significant. AUC results were MSKCC: 0.713/Tenon: 0.671/Stanford: 0.534/DEU: 0.814. Conclusions: The MSKCC nomogram proved to be a good discriminator of NSLN metastasis in SLN positive BC patients for our population. Stanford and Tenon nomograms were not as predictive of NSLN metastasis. Our newly created formula was the best prediction tool for discriminate of NSLN metastasis in SLN positive BC patients for our population. We recommend that nomograms be validated before use in specific populations, and more than one validated nomogram may be used together while consulting patients.

• Journal of Korean Academy of Nursing
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• v.33 no.8
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• pp.1093-1110
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• 2003

Purpose. The number of elders in institutions has increased as family supporting systems have changed in Korea. The purpose of this study were to understand the life satisfaction among elders in institutions and to identify the factors influencing on life satisfaction. Methods. The instruments used were Yun(1982)'s scale modified Memorial University of Newfoundland Scale for Happiness(MUNSH) in life satisfaction, ADL and IADL in activity level, Self-rating Depression Scale(SDS) in depression and Norbeck Social Support Questionnaire(NSSQ) scale in social support. Also, Perceived health status was measured by Visual Graphic Rating Scale. The subject of this study is 107 cognitively intact and ambulatory elders in 7 institutions in Daegu city and Kyungpook province. The data have been collected from May 1 to June 30, 2001. For the analysis of collected data, frequency analysis, mean, standard deviation, Pearson's correlation and stepwise multiple regression analysis were used for statistical analysis by SPSS win(version 9.0) program. Results. Life satisfaction for the elders in institutions showed negative correlation with SDS, and positive correlation with activity level. The regression form of the stepwise multiple regression analysis to investigate the influencing factors of life satisfaction for the elders in institutions was expressed by y =90.988-0. 733x1-0.188x2-0.069x3-0.565x4 (xl: SDS x2: Social support x3: Activity level x4: Monthly pocket Money) and 57.9% of varience in life satisfaction was explained by the model. Conclusion. The factors influencing on life satisfaction among the elders in institutions were SDS, social support, activity level and monthly pocket money. According to the results of this study, depression, social support and activity level are considered the prime causal factors for life satisfaction.

• The Journal of the Korea Contents Association
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• v.14 no.7
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• pp.324-333
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• 2014

This study was conducted to investigate the factors affecting the social anxiety in nursing students. A total of 227 nursing students participated in the study. Data were analyzed by frequencies, t-test, ANOVA, Pearson correlation coefficient, and multiple stepwise regression with SPSS WIN 18.0. The mean scores for problem solving ability and social anxiety were at the intermediate level. Problem solving ability negatively correlated with social anxiety. The significant predictors of social anxiety included cognitive reaction within the seven problem solving ability subscales and perceived interpersonal relationship. The regression model explained 22.6% of social anxiety. As a result, to decrease social anxiety in nursing students, nursing educators should develop educational intervention programs to change cognitive distortions presented in unfamiliar social situations and improve interpersonal relationships ability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1615-1622
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• 2014

The purpose of this study was to understand the factors affecting the academic resilience in nursing students. This study involved 280 second year nursing students. Data were collected from March 4 to 8 in 2013. Data were analyzed with frequency, ANOVA, Scheffe's test, person's correlation coefficients, and Stepwise multiple regression with using SPSS version 20.0. The score of academic resilience, problem solving abilities and communication skill in nursing students were 3.67, 3.53 and 3.50 respectively. The results showed that the higher problem solving abilities and communication skills, the higher academic resilience. The factors that significantly affected academic resilience were plan/practice, analytic ability, goal creation ability, cause analysis and performance/assessment accounting for 50.7% of the regression model. Therefore, it is necessary to consider the description of nursing students' academic resilience factors to develop effective education program.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.157-166
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• 2019

This study investigates the influence of emotional intelligence and professional self-concept on retention intention in nurses. The participants were 170 nurses from three hospitals in B city. Data were collected with structured questionnaires, from May 7 to May 30, 2019, and analyzed by applying descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and stepwise multiple regression, using the SPSS/WIN 24.0 program. Our results reveal the level of emotional intelligence to be 4.82(7), professional self-concept 2.61(4), and retention intention 5.47(8). We observed that the retention intention positively correlates with emotional intelligence and professional self-concept. The factors influencing retention intention were professional self-concept (β=.456, p<.001), age (β=-.228, p<.001) and holidays (β=.197, p=.002). This regression model determines the intention retention to be 35.9%. We propose that it is necessary to develop and make available programs embracing factors that prevent and reduce retention intention in nurses.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.35-62
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• 1995

Traffic has a very important function but has caused such social problems as traffic congestion parking and traffic accidents in metropolitan areas. It is difficult to examine the causes of traffic accidents related to human life, which occur by human, vehicle and environmental factors. But human factor is the only measure requlating these factors together an analyzing factors influencing establishment of counterplan of traffic accidents. Consequently , this study employs the principal component analysis and stepwise multiple regression analysis to estimate the characteristics and influential factors of traffic accidents and defines the typical patterns of happening lots of traffic accidents. Accordingly, this study establishes an acknowledgement model of the causes and preference model of the counterplan of traffic accidents using Multi-Dimension Preference(MDPREF) method.

• Structural Engineering and Mechanics
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• v.66 no.6
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• pp.677-691
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• 2018

Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.1-7
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• 2008

This study quantified 7 trunk muscles' physiological cross-sectional areas (PCSAs) and developed prediction equations for the physiological cross-sectional area as a function of anthropometic variables for Korean people. Nine females and nine males were participated in the magnetic resonance imaging (MRI) scans approximately from S1 through T8. Muscle fiber angle corrected cross-sectional areas (anatomical cross sectional areas: ACSAs) were recorded at each vertebral level and maximum value of ACSAs were determined as physiological cross sectional area (PCSA). There was a significant gender difference in PCSAs of all muscles (p<0.05). Stepwise linear regression techniques using anthropometric measures (e.g., height, weight, trunk depths and widths) as independent variables were conducted to develop prediction equations for the PCSA for each muscle. For males, six muscles' significant prediction equations (p<0.05) were developed except quadratus lumborum. For females, three prediction equations were developed for psoas, quadratus lumborum, and erector spinae muscles (p<0.05).

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.691-699
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• 2011

Land sliding is one of well-known nature disaster. As a part of effort to reduce damage from land sliding, many researchers worked on increasing prediction ability. However, because previous studies are conducted mostly by non-statisticians, previously proposed models were hardly statistically justifiable. In this paper, we predicted the probability of land sliding using the logistic regression model. Since most explanatory variables under consideration were correlated, we proposed the final model after backward elimination process.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.