• Journal of Digital Convergence
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• v.13 no.12
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• pp.245-257
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• 2015

This study was to develop the predictive model for severity-adjusted mortality of inpatients with multiple chronic conditions and analyse the factors on the variation of hospital standardized mortality ratio(HSMR) to propose the plan to reduce the variation. We collect the data "Korean National Hospital Discharge In-depth Injury Survey" from 2008 to 2010 and select the final 110,700 objects of study who have chronic diseases for principal diagnosis and who are over the age of 30 with more than 2 chronic diseases including principal diagnosis. We designed a severity-adjusted mortality predictive model with using data-mining methods (logistic regression analysis, decision tree and neural network method). In this study, we used the predictive model for severity-adjusted mortality ratio by the decision tree using Elixhauser comorbidity index. As the result of the hospital standardized mortality ratio(HSMR) of inpatients with multiple chronic conditions, there were statistically significant differences in HSMR by the insurance type, bed number of hospital, and the location of hospital. We should find the method based on the result of this study to manage mortality ratio of inpatients with multiple chronic conditions efficiently as the national level. So we should make an effort to increase the quality of medical treatment for inpatients with multiple chronic diseases and to reduce growing medical expenses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.1100-1108
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• 2009

The Objectives: The purpose of this study is to identify the factors related to management of DM in Korea. Methods: The subjects selected by using data of National Health and Nutrition Survey(NHANS) in 2005 were 415 adults, aged 20 and older, and diagnosed with DM. This study used data mining algorithms. This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and Neural Network on the basic of validation, it was found that the model performance of decision tree was the best among the above three techniques. Result: First, awareness of DM was positively associated with age, residential area, and job. The most important factor of DM awareness is age. Awareness rate of DM with 52 age over is 76.1%. Among the ${\geq}52$ age group, an important factor is family history. Among patients who are 52 years or over with family history of DM, an important factor is job. The awareness rate of patients who are 52 age over, family, history of DM, and professionals is 95.0%. Second, treatment of DM was also positively associated with awareness, region, and job. The most important factor of DM treatment is DM awareness. Treatment rate of patients who are aware of DM is 84.8%. Among patients who have awareness of DM, an important factor is region. The awareness rate of patients who are aware of DM in rural area is 10.4%. Conclusion: Finally, the result of analysis suggest that DM management programs should consider group characteristic of DM patients.

• Journal of the Korean Society of Radiology
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• v.11 no.3
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• pp.131-137
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• 2017

This study investigated the risk factor of Gallbladder stone in Busan and Kyungnam area. The subjects of the experiment was performed with patients by abdominal ultrasonography among the patients who came to the P hospital from June 2016 to December 2016. Among them, risk factors were analyzed on 353 people at the same time when abdominal ultrasonography and hematological test. The statistical analysis of risk factors related to the Gallbladder stone was performed by independent t-test and chi-square test. In consider of difference verification result for calculations odds ratio about independent variables, multiple logistic regression analysis to conduct verify adequacy by calculating forecasting model from variable. As a result, Gallbladder stone risk factors have relevance to age ${\gamma}GTP$ with probability model and values to calculated. Age was showed sensitivity 49.7%, specificity 82.2%, receiver operating characteristic area under curve 0.724. Forecasting probability sensitivity 69.3%, specificity 62.4%, receiver operating characteristic area under curve 0.699 showed, ${\gamma}GTP$ confirmed validity of forecasting model.

• Journal of radiological science and technology
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• v.39 no.3
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• pp.353-359
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• 2016

This study were to evaluate risk factors of GB polpy in Busan and Gyeongnam area. This study was performed with patients by abdominal ultrasonography among the patients who came to the P hospital from January to May 2016. Among them, risk factors were analyzed on 399 people at the same time when abdominal ultrasonography and hematological test. The statistical analysis of risk factors related to the GB ployp was performed by independent t-test and chi-square test. In consider of difference verification result for calculations odds ratio about independent variables, multiple logistic regression analysis to conduct verify adequacy by calculating forecasting model from variable. As a result, GB polyp risk factors have relevance to male, HBsAg positive, triglyceride. GB polyp risk factors confirmed to male, HBsAg positive, triglyceride were calculated forecasting model and forecasting probability value. Forecasting probability sensitivity 61.0%, specificity 76.8%, ROC area under curve 0.735 showed, it confirmed validity of forecasting model. When analyzing the GB polyps morphologically, among the GB polyp types observed from abdominal ultrasonography, the hyperechoic and homogeneous pattern with neck was the largest as shown from 27.5% and two GB polyps were shown most from 38%, sizes were shown most by maximum diameter, 5 to 10mm from 53%. As a disease accompany with GB polyp showed mild fatty liver(23%), diffuse hepatopathy(21%).

• Journal of Digital Convergence
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• v.14 no.2
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• pp.325-332
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• 2016

The purpose of this study is to investigate the attributes influencing the prevalence of metabolic syndrome and develop the prediction model for metabolic syndrome over 40-aged people from Korea Health and Nutrition Examination Study 2012. The researcher chose the attributes for prediction model through literature review. Also, we used the decision tree, logistic regression, artificial neural network of data mining algorithm through Weka 3.6. As results, social economic status factors of input attributes were ranked higher than health-related factors. Additionally, prediction model using decision tree algorithm showed finally the highest accuracy. This study suggests that, first of all, prevention and management of metabolic syndrome will be approached by aspect of social economic status and health-related factors. Also, decision tree algorithms known from other research are useful in the field of public health due to their usefulness of interpretation.

• Communications of Mathematical Education
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• v.31 no.2
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• pp.187-201
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• 2017

The study in this paper, which is part of a bigger study investigating non-cognitive characteristics of Korean students at the 4-12 grade levels, aims to identify the influential characteristics that explain students' decision to give up on mathematics learning. We consider seven non-cognitive student characteristics: value, interest, attitudes, external motivation, internal motivation, learning conation and efficacy. Data were collected from 21,485 Korean students, and were analyzed with a logistic regression method using SPSS. The findings show that efficacy was the most significant indicator of students' decision to give up on mathematics learning in all three grade level bands: elementary (4th-6th), middle (7th-9th) and high (10th-12th). In particular, the causal model analysis shows that students who highly value mathematics tend to have stronger internal and external motivation, which bring about stronger interest and learning conation, which in turn lead to positive attitudes and strong efficacy regarding the learning of mathematics. It was further found that while external motivation was a significant indicator of upper grade level students' decision to give up on mathematics learning, it was only a moderate indicator for lower grade level students. The findings of this study provide useful information about which non-cognitive areas need to be focused on, in what grade levels, to help students stay on track and not fall behind in learning mathematics.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.217-230
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• 2019

This study was conducted to develop a customized severity-adjustment method and to evaluate their validity for acute myocardial infarction(AMI) patients to complement the limitations of the existing severity-adjustment method for comorbidities. For this purpose, the subjects of KCD-7 code I20.0 ~ I20.9, which is the main diagnosis of acute myocardial infarction were extracted using the Korean National Hospital Discharge In-depth Injury survey data from 2006 to 2015. Three tools were used for severity-adjustment method of comorbidities : CCI (charlson comorbidity index), ECI (Elixhauser comorbidity index) and the newly proposed CCS (Clinical Classification Software). The results showed that CCS was the best tool for the severity correction, and that support vector machine model was the most predictable. Therefore, we propose the use of the customized method of severity correction and machine learning techniques from this study for the future research on severity adjustment such as assessment of results of medical service.

• The Journal of Bigdata
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• v.3 no.2
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• pp.71-78
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• 2018

The study is based on machine learning techniques to increase the accuracy of the forest fire predictive model. It used 14 years of data from 2003 to 2016 in Gang-won-do where forest fire were the most frequent. To reduce weather data errors, Gang-won-do was divided into nine areas and weather data from each region was used. However, dividing the forest fire forecast model into nine zones would make a large difference between the date of occurrence and the date of not occurring. Imbalance issues can degrade model performance. To address this, several sampling methods were applied. To increase the accuracy of the model, five indices in the Canadian Frost Fire Weather Index (FWI) were used as derived variable. The modeling method used statistical methods for logistic regression and machine learning methods for random forest and xgboost. The selection criteria for each zone's final model were set in consideration of accuracy, sensitivity and specificity, and the prediction of the nine zones resulted in 80 of the 104 fires that occurred, and 7426 of the 9758 non-fires. Overall accuracy was 76.1%.

• Journal of Wetlands Research
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• v.23 no.2
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• pp.122-132
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• 2021

We reviewed the methods employed in Korean tidal flat surveys to measure the local abundance of the endangered wildlife and marine protected species, the fiddler crab, Austruca lactea. A complete census for infinite population is impossible even in a limited habitat within a tidal flat, and density estimates from samples strongly vary due to diverse biological and ecological factors. The habitat boundaries and areas shift with periodicities or rhythmic activities of organisms as well as measurement errors. Hence the local abundance calculated from density and habitat areas should be regarded as transient. This conjecture was valid based on the spatio-temporal variations of the density averages, standard error ranges, and spatial distribution of the crab, A. lactea observed for 3 years (2015-2017) in Songdo tidal flat in Incheon. We proposed the potential habitat areas using the occurrence probability of 50% from logistic regression model, reflecting the importance of habitat conservation value as an alternative to local abundance. The spatial shape of potential habitat predicted from a generalized model would remain constant over time unless the species' critical environmental conditions change rapidly. The species-specific model is expected to be used for the introduction of desired species in future habitat restoration/creation projects.

• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.85-112
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• 2013

This study aims to understand the developmental trajectories of life satisfaction among retirees and to examine what factors differentiate different trajectory classes. This study used three waves of longitudinal data from Korean Retirement and Income Study and data collected every two years(2005, 2007, and 2009). Subjects were respondents aged 50-69 who identified to be retired between wave 1 and wave 2. Finally, this study used 243 respondents for final data analysis. Life satisfaction was measured by seven items. The latent class growth model and multiple logistic regression model were used for data analysis. This study identified three distinct trajectory classes: high stable class(47.7%), high at the early stage but decreased class(42.8%), and low at the early stage and then decreased class(9.5%). This study founded that approximately 50% of the retirees experienced the decline of life satisfaction after retirement and about 10% of the sample was the most vulnerable group. This study analyzed what factors make different among the distinct trajectory groups. As a results, retirees who experienced the improvement in health change were more likely to be in 'high stable class' compared to 'hight at the early stage but decreased class'. In addition, retirees who were less educated, maintained the same health status rather than the improvement, worked as a temporary or a day laborer, and had less household income were more likely to belong to 'low at the early stage and then decreased class' relative to 'high stable class'. This study suggests that there are distinct three trajectories on life satisfaction among the retirees and finds out factors differentiating between trajectory groups. Based on these findings, the study discusses the implications for social work practice and further study.