• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.271-280
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• 2019

Smart factory, a critical part of digital transformation, enables data-driven decision making using monitoring, analysis and prediction. Predictive maintenance is a key element of smart factory and the need is increasing. The purpose of this study is to analyze the degradation characteristics of a galvanizing kettle for the steel plating process and to predict the remaining useful life(RUL) for predictive maintenance. Correlation analysis, multiple regression, principal component regression were used for analyzing factors of the process. To identify the trend of degradation, a proposed rolling window was used. It was observed the degradation trend was dependent on environmental temperature as well as production factors. It is expected that the proposed method in this study will be an example to identify the trend of degradation of the facility and enable more consistent predictive maintenance.

• Honam Mathematical Journal
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• v.43 no.4
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• pp.717-739
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• 2021

We used blood data to predict obesity by complementing the BMI risk, because some blood factors are significantly associated with obesity. For the sampling method, a two-step stratified colony sampling method was used based on sixteen blood factors collected by the Korea National Health and Nutrition Examination Survey(KNHANES). We identify the number of effective blood data of obesity in the final model as 6 ~ 8 factors that differ somewhat depending on age and gender. Also, the coefficient of determination that represents the predictive power of obesity in the regression model is the highest for both men and women of aged 19 and in their 20s and 30s, and the predictive power decreases with increasing age.

• Korean Journal of Childcare and Education
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• v.17 no.5
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• pp.83-103
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• 2021

Objective: This study aimed to develop a longitudinal predictive model that identifies first-grade children who are at risk for ADHD and to investigate the factors that predict the probability of belonging to the at-risk group for ADHD by using machine learning. Methods: The data of 1,445 first-grade children from the 1st, 3rd, 6th, 7th, and 8th waves of the Korean Children's Panel were analyzed. The output factors were the at-risk and non-risk group for ADHD divided by the CBCL DSM-ADHD scale. Prenatal as well as developmental factors during infancy and early childhood were used as input factors. Results: The model that best classifies the at-risk and the non-risk group for ADHD was the LASSO model. The input factors which increased the probability of being in the at-risk group for ADHD were temperament of negative emotionality, communication abilities, gross motor skills, social competences, and academic readiness. Conclusion/Implications: The outcomes indicate that children who showed specific risk indicators during infancy and early childhood are likely to be classified as being at risk for ADHD when entering elementary schools. The results may enable parents and clinicians to identify children with ADHD early by observing early signs and thus provide interventions as early as possible.

• The Korean Journal of Pain
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• v.35 no.4
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• pp.447-457
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• 2022

Background: Ultrasound-guided genicular nerve radiofrequency (RF) procedures are of interest in the management of chronic knee pain. A wide variety of demographic, clinical, and procedural characteristics can affect treatment success. This study aimed to determine predictive factors to provide superior treatment outcomes. Methods: The demographic, clinical, and technical data of patients who received genicular nerve RF for knee pain between September 2016 and September 2021 were evaluated. A positive outcome was defined as at least 50% pain relief on a pain score for at least 6 months. Logistic regression analysis was performed to determine the factors associated with a successful response to genicular RF. Results: Among 206 patients who underwent genicular RF, 62% of the patients reported successful outcomes at 6 months. In the multivariate model, targeting 5 nerves (odds ratio [OR], 6.184; 95% confidence interval [CI], 2.291-16.690; P < 0.001) was the most significant predictor of successful outcomes. Multivariable logistic regression analysis showed that prognostic genicular nerve block with a 50% cut-off value (OR, 2.109; 95% CI, 1.038-4.287; P = 0.039), no opioid use (OR, 2.753; 95% CI, 1.405-5.393; P = 0.003), and depression (OR, 0.297; 95% CI, 0.124-0.713; P = 0.007) were the predictive factors significantly associated with response to genicular RF. Conclusions: Clinical and technical factors associated with better treatment outcomes were ultimately targeting more nerves, performing prognostic block, no opioid use, and no depression. These results are expected to be considered when selecting patients for genicular RF.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.139-145
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• 2017

The purpose of this study is to establish a predictive causal model of codependency that is a main issue of family problem on the base of Triple ABC-X model which is a kind of family stress model. For the purpose of this study, we reviewed the concept and characteristics of codependency, affecting factors of codependency, and then reviewed the basic concept and logic of Triple ABC-X Model as theoretical viewpoint for the purpose of establishing a predictive causal model of codependency. We established it through examining main variables of codependency from Triple ABC-X Model. Main ingredients of the predictive causal model include boundary ambiguity, internal working model, internal and external locus of control, self-regard, social support, individual maladjustment etc. We established a predictive model of codependency basing on logic inferences among the variables. This study is expected to be used basic data to introduce some implications and for hereafter research.

• 대한생식의학회:학술대회논문집
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• 2002.05a
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• pp.38-39
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• 2002

• Clinical and Experimental Pediatrics
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• v.62 no.4
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• pp.124-125
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• 2019

• Journal of Korean society of Dental Hygiene
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• v.17 no.1
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• pp.63-76
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• 2017

Objectives: The purpose of this study is to investigate the factors affecting medical knowledge and practice of dental treatment for systemic disease among dental health care workers. Methods: A self-reported questionnaire was filled out by 222 dental health care workers working in Seoul, Daejeon, Busan, Gyeonggi province, Chungcheong province, and Jeolla province within the period between May 1 - June 30, 2016. Knowledge and medical knowledge about the clinical treatment of patients suffering from systemic disease and their practice were composed of items that were corrected, supplemented, and developed by themselves based on previous research. Results: Factors affecting knowledge about clinical treatment of patients suffering from systemic disease were place of employment, treatment about systemic disease, and practice of dental treatment for systemic diseases. Predictive power was 38.5%. Factors affecting practice of clinical treatment of patients suffering from systemic disease were sex, place of employment, treatment about systemic disease, the basic equipment and drugs needed for emergency care, and knowledge of dental treatment for systemic diseases. Predictive power was 39.1%. Conclusions: Dental health care workers' knowledge and practice of dental treatment of patients suffering from systemic diseases were important factors influencing each other.

• Journal of Korean Clinical Nursing Research
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• v.24 no.3
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• pp.347-355
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• 2018

Purpose: The purpose of this study was to understand morality identity, occupational stress and authentic leadership and identify factors contributing to turnover intention among intensive care unit (ICU) nurses. Methods: Data were collected from 230 nurses at the university hospitals in Daegu, Ulsan and Busan between February 15 and March 23, 2017. Instruments measuring turnover intention, moral identity, occupational stress, and authentic leadership were utilized. Statistical analysis included t-test, ANOVA, Pearson correlational analysis, and hierarchical regression analysis. Results: A total of 207 nurses in ICU participated in this study. The power of explanation with age and dependents on turnover intention was 4.1%. With inclusion of occupational stress, moral identity, and authentic leadership factors put into the model, further 20.4% was explained. The explanatory power of the turnover intention in the final model was 23.6% (F=11.63 p<.001), and occupational stress was the key factor explaining turnover intention (${\beta}=.28$, p<.001). Predictive factors contributing to turnover intention were age, occupational stress, moral identity, and authentic leadership in final model. Conclusion: These findings demonstrated occupational stress, moral identity and authentic leadership as critical factors contributing turnover intention of ICU nurses. It is necessary to promote nursing manager's authentic leadership, and to encourage moral identity in ICU nurses. In addition, providing intervention programs to reduce occupational stress for ICU nurses is necessary.