• Proceedings of the Safety Management and Science Conference
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• 2006.04a
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• pp.51-56
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• 2006

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare performance of algorithms for data analysis of industrial accidents and this paper provides a comparative analysis of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. In this study, data on 67,278 accidents were analyzed to create risk groups for a number of complications, including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years $(2002\sim2004)$ in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

• Proceedings of the Safety Management and Science Conference
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• 2006.11a
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• pp.293-297
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• 2006

Recently most universities are suffering from students leaving their majors. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, this paper uses decision tree algorithm which is one of the data mining techniques which conduct grouping or prediction into several sub-groups from interested groups. This technique can analyze a feature of type on students leaving their majors. The dataset consists of 5,115 features through data selection from total data of 13,346 collected from a university in Kangwon-Do during seven years(2000.3.1 $\sim$ 2006.6.30). The main objective of this study is to evaluate performance of algorithms including CHAID, CART and C4.5 for classification of students leaving their majors with ROC Chart, Lift Chart and Gains Chart. Also, this study provides values about accuracy, sensitivity, specificity using classification table. According to the analysis result, CART showed the best performance for classification of students leaving their majors.

• Journal of the Korea Safety Management & Science
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• v.8 no.3
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• pp.1-9
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• 2006

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare algorithms for data analysis of industrial accidents and this paper provides an optimal predicting model of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. Also, this paper provides an approximation model for an optimal predicting model based on NN. The approximation model provided in this study can be utilized for easy interpretation of data analysis using NN. This study uses selected ten independent variables to group injured people according to a dependent variable in a way that reduces variation. In order to find an optimal predicting model among 5 algorithms, a retrospective analysis was performed in 67,278 subjects. The sample for this work chosen from data related to industrial accidents during three years ($2002\;{\sim}\;2004$) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.17-25
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• 2006

Nowadays most colleges are confronting with a serious problem because many students have left their majors at the colleges. In order to make a countermeasure for reducing major separation rate, many universities are trying to find a proper solution. As a similar endeavor, the objective of this paper Is to find a predicting model of students leaving their majors. The sample for this study was chosen from a university in Kangwon-Do during seven years(2000.3.1 $\sim$ 2006. 6.30). In this study, the ratio of training sample versus testing sample among partition data was controlled as 50% : 50% for a validation test of data division. Also, this study provides values about accuracy, sensitivity, specificity about three kinds of algorithms including CHAID, CART and C4.5. In addition, ROC chart and gains chart were used for classification of students leaving their majors. The analysis results were very informative since those enable us to know the most important factors such as semester taking a course, grade on cultural subjects, scholarship, grade on majors, and total completion of courses which can affect students leaving their majors.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.29 no.4
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• pp.172-177
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• 2018

Objectives: We examined the agreement between the Autism Diagnostic Observation Schedule (ADOS) and the Childhood Autism Rating Scale (CARS) in the diagnosis of autism spectrum disorder. Methods: The ADOS and CARS scores of 78 children were retrospectively collected from a chart review. A correlation analysis was performed to examine the concurrent validity between the two measures. Using the receiver operating characteristic (ROC) curve, we determined the optimal cut-off score of the CARS for identifying autism spectrum disorder. Results: The CARS score was significantly correlated with the ADOS score (r=0.808, p<0.001). Taking ADOS as the ideal standard, the optimal cut-off scores of CARS for identifying autism and autism spectrum were 30 and 24.5, respectively. Conclusion: We determined the optimal cut-off scores of CARS for screening and diagnosing autism spectrum disorder.

• Journal of East-West Nursing Research
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• v.28 no.1
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• pp.10-20
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• 2022

Purpose: We compared the Korean Triage and Acuity Scale (KTAS), Injury Severity Score (ISS), and Revised Trauma Score (RTS) determined the validity of KTAS for classifying trauma patients. Methods: A retrospective chart review of 10,865 trauma patients (aged ≥15 years) who visited a single regional trauma and emergency medical center from January 1, 2016, to December 31, 2020, was conducted. Data were collected from the Korean Trauma Data Bank. Based on KTAS classification, the rates of intensive care unit admission, surgery and intervention, transfusion, emergency room (ER) and hospital mortality, and ER stay time were investigated. Data were analyzed using Chi-square test, Pearson's correlation coefficient, receiver operating characteristic curve, and area under the ROC curve. Results: In the KTAS, severe trauma patients (ISS ≥16) were classified as Level 1 (79.6%), 2 (44.8%), 3 (15.5%), 4 (4.0%) and 5 (7.6%). The following were the predictive powers of KTAS, ISS, and RTS for different parameters: surgery and intervention rate, KTAS (.71), ISS (.70), and RTS (.63); transfusion rate within 4h, KTAS (.82), ISS (.82), and RTS (.74); ER stay time within 90 min, KTAS (.72), ISS (.62), and RTS (.56); and ER mortality, KTAS (.84), ISS (.72), and RTS (.88). These findings were statistically significant (p<.001). The sensitivity and specificity of KTAS for trauma patients were .88 (.87~.90), and .38 (.37~.39), respectively. Conclusion: KTAS is a useful classification system that can predict the clinical outcomes of patients with trauma, and effectively triage acutely ill trauma patients, thus provide appropriate treatment.