• Journal of agricultural medicine and community health
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• v.24 no.2
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• pp.245-268
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• 1999

It is necessary that the old should have the physical and social ability to perform their daily life. This study is to grasp their degree of disability and problems and suggest their solutions. It surveyed the 87 old people over 65 years old from September 1st until September 30th, in 1997. The findings are as follows. 1) The activities of daily living(ADL) to find their degree of physical disability shows that their average performance ability is 75.9% of all the action while 24.1% of all the old people needs the others' help. As they get older and older, the aged drop off in their physical ability, which is related to a statistical sense (p<0.001). 2) The social disability shows that the aged have their great difference from 9.2% to 85.1% in their instrumental activities of daily living(IADL), intellectual ability and social role. 3) A simple analysis shows that the activities of daily living are, in a statistical sense, related to age(p<0.001), the use of elder's hall(p<0.001), the understanding degree of health(p<0.01) and so forth. 4) A simple analysis shows that the instrumental activities of daily living are, in a statistical sense, related to age(p<0.001), the degree of education(p<0.05), the life of leisure(p<0.001), the understanding degree of health and so forth. 5) A multivariate logistic regression analysis shows that the disability of daily living is related to age, the visit of elder's hall, the period of solitary living, instrumental activities of daily living is age and the visit of elder's hall, and social role is the visit of elder's hall and the decree of education, while intellectual activity has no related variables in a statistical sense.

• Journal of Preventive Medicine and Public Health
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• v.25 no.2 s.38
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• pp.127-140
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• 1992

This study was done to identify the association between serum copper and zinc levels and the cirrhosis and hepatocellular carcinoma(HCC), and to evaluate its diagnostic value on liver diseases. Sixty-three healthy persons, 60 patients with cirrhosis and 33 patients with hepatocellular carcinoma were rendomly selected and investigated for their general characteristics from October 1990 to August 1991. For analysis of the biochemical markers in liver function test and the serum copper and zinc levels, their fasting venous blood were sampled at 9:00 to 11:00 in the morning and centrifuged to separate the serum within one hour. All the samples were immediately analysed for biochemical markers and stored at $-20^{\circ}C$ in polypropylene tubes further copper and zinc analysis. Mean of serum coppper levels was $91.97{\pm}4.76{\mu}g/dl$ in control, $106.21{\pm}2.73{\mu}g/dl$ in cirrhosis and $127.05{\pm}0.77{\mu}g/dl$ in HCC. The value of HCC was statistically significantly higher than that of the control and cirrhosis(p<0.05). Serum zinc levels were $110.82{\pm}7.24{\mu}g/dl$ in control, $68.10{\pm}5.43{\mu}g/dl$ in cirrhosis and $63.78{\pm}2.20{\mu}g/dl$ in HCC. The values of cirrhosis and HCC were statistically significantly lower than that of control(p<0.05). The Cu/Zn ratio was statiatically significantly different among three groups(p<0.05). Test total protein, albumin, ALP and total bilirubin of biochemical markers of liver function were statistically significantly different among three groups(p<0.05). Differences between cirrhosis and HCC for ALT and AST, and between the control and HCC for direct bilirubin were not statistically significant. Biochemical markers statistically significantly correlated with serum copper and zinc levels and Cu/Zn ratio(p<0.05), were variable in three groups. In multiple logistic regression, odds ratio of serum copper level and Cu/Zn ratio had no statistical significance on the cirrhosis and the HCC, but that of serum sinc was statistically significant as 0.951 and 0.952(p<0.05). Serum copper and zinc levels and Cu/Zn ratio were not statistically significantly different between the cirrhosis and HCC. H\Albumin, ALP, zinc, total bilirubin and age among all variables were selected as main variables for three-group discriminant analysis. Percentage of 'grouped' cases correctly classified by these five variables was 98.4 for control, 73.4 for cirrhosis, 75.7 for HCC and 84.0 for all subjects. This study suggests that zinc level is considered to play a role as diagnostic marker on the hepatic disorders and be more useful than serum copper level and Cu/Zn ratio in diagnosis of the liver diseases.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of Yeungnam Medical Science
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• v.20 no.1
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• pp.62-70
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• 2003

Background: Most of all studies about the relation between the health risk and obesity are based on the European and American data. The purpose of this study is to examine the relation between adiposity and risk factors for cardiovacular disease (CVD) in normal weight individuals. Materials and Methods: Normal weight subjects with a body mass index (BMI) between 18.5 and $23kg/m^2$ (76 subjects) and overweight subjects with a BMI between 23 and $25kg/m^2$ (53 subjects) were retained for this study. Normal weight subjects were divided into three group of each adiposity variable, then three group and the overweight group were evaluated for the presence of CVD risk factors and analyze the correlation coefficients between adiposity variables and risk factors controlled for age in normal weight, overweight groups. Using logistic regression analysis, the odds ratio (OR) for the prevalence of risk factors for each group of adiposity variables and the overweight group was estimated relative to the first group in normal weight subjects. Results: Systolic BP, diastolic BP, LDL cholestrol, HDL cholesterol, triglycerides in normal weight subjects were significantly correlated with all adiposity variables (P<0.01). Third group (3.7 for %fat and 4.7 for fat mass)of adiposity variables in the normal weight group and the overweight group (6.6 for %fat and 11.5 for fat mass) tended to have higher ORs compared to first group for risk factor variables. Conclusion: Normal weight subjects with elevated adiposity had higher prevalence of risk factors than normal weights subjects with less adiposity. Measuring of adiposity added additional information of cardiovascular disease risk factors in normal weight subjects.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.221-237
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• 2011

This study aims to investigate the environmental equity of the accessibility to urban neighborhood parks in the city of Daegu. The spatial distribution of urban neighborhood parks was explored by spatial statistics and the spatial accessibility to them was then evaluated by both minimum distance and coverage approaches. Descriptive and inferential statistics such as proximity ratio, Mann Whitney U test, and logistic regression were used for comparing the socioeconomic characteristics over different accessibilities to the neighborhood parks and then testing the distributional inequity hypothesis. The results from the minimum distance method indicated that Dalseo-gu had the best accessibility to the neighborhood parks while Dong-gu had the worst accessibility. It was apparent with the coverage method that Dalseo-gu had the best accessibility whereas Dong-gu and Nam-gu had the worst accessibility to the neighborhood parks at 500m and 1,000m buffer distances. There existed the spatial pattern of environmental inequity in old towns with respect to population density and the percentage of people under the age of 18. The spatial pattern of environmental inequity in new towns was explored on the basis of the percentage of people over the age of 65, the percentage of people below the poverty level, and the percentage of free of charge rental housing. These results were closely related to the development process of urban parks in Daegu stimulated by the quantitative urban park policy, urban development process, and residential location pattern such as permanent rental housing and free of charge rental housing. This study further extends the existing research topics of environmental justice related to the distributional inequity of environmental disamenities and hazards by focusing on environmental amenities such as urban neighborhood parks. The results from this study can be used in making the decisions for urban park management and setting up urban park policy with considering the social geography of Daegu.

• Journal of agricultural medicine and community health
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• v.38 no.3
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• pp.182-194
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• 2013

Objectives: To identify and compare the health behaviors and health status of the elderly between urban and rural areas using the data of the Korean National Health and Nutrition Examination Survey (KNHANES). Methods: The study population comprised 3,823 elderly people aged 65 years or older who participated in the $4^{th}$ KNHANES (2007-2009). The areas were classified into "large cities," "cities," and "rural areas" using the administrative and residential areas. The health behaviors and health status of the elderly between the rural and urban areas were compared using a complex sample design with the Rao-Scott chi-square test and weighted multiple logistic regression analysis. Results: Compared to large cities, the odds ratios (ORs) (95% confidence interval [CI]) of rural areas were as high as 1.58 (1.25-2.01) for the influenza vaccination and as low as 0.47 (0.37-0.59) for flexibility exercises, 0.56 (0.38-0.81) for muscular exercises, and 0.76 (0.62-0.92) for obesity. The ORs (CI) for osteoarthritis and diabetes mellitus were as low as 0.81 (0.66-0.99) and 0.70 (0.55-0.89), respectively. Conclusions: The health behaviors and health status of the elderly are better in rural areas than in urban areas despite the fact that the socioeconomic conditions in rural areas are poorer that those in urban areas. These findings suggest that programs suitable for residential areas should be developed and that studies to explain the differences in residential areas are needed.

• Journal of Nutrition and Health
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• v.48 no.4
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• pp.352-363
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• 2015

Purpose: Despite the popularity of dietary supplements, little data are available on their use by university students. The purpose of this study was to examine the use of vitamin mineral supplements and to identify factors related to supplement use among university students. Methods: University students (N = 345) in Seoul were surveyed. Survey questions included descriptive demographics, types of vitamin and mineral supplements used, health related lifestyle factors, mini dietary assessment, and knowledge and behaviors related to supplement use. Results: Of university students surveyed, 41% consumed vitamin and mineral supplements. Among the supplement users, multivitamins were the most commonly used dietary supplements (68.6%), followed by vitamin C (31.4%) and calcium (17.1%). In particular, the use of vitamin C and iron supplements was more common in females than males (p < 0.05). For the number of supplements taken daily, 32.1% of supplement users consumed 2 or more supplements; 20% of supplement users had almost no knowledge of the supplements being taken. Based on the results of multivariable logistic regression analysis, supplement use was associated with higher interest in their own health, non-smoker, and supplement use by family (p < 0.05). In addition, supplement use was slightly associated with healthy dietary behavior such as consuming a variety of foods (p = 0.05) and current disease status (p = 0.05). Conclusion: University students with relatively healthy lifestyles appear to take vitamin and mineral supplements, but they had little knowledge of the supplements. Given high prevalence of dietary supplement use among university students, nutrition education regarding supplement use is needed.

• Journal of dental hygiene science
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• v.17 no.1
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• pp.38-45
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• 2017

We evaluated the association between sleep duration, dental caries, and periodontitis by using representative nationwide data. We examined 8,356 subjects aged ${\geq}19$ years who participated in the sixth Korea National Health and Nutrition Examination Survey (2013~2014). Sleep duration were grouped into ${\leq}5$, 6, 7, 8, and ${\geq}9$ hours. Presence of dental caries was defined as caries in ${\geq}1$ permanent tooth on dental examination. Periodontal status was assessed by using the community periodontal index (CPI), and a CPI code of ${\geq}3$ was defined as periodontitis. A chi-square test and multiple logistic regression analysis were used to determine statistical significance. Model 1 was adjusted for age and sex, model 2 for household income, educational level, and marital status plus model 1, and model 3 for smoking status, alcohol consumption, blood pressure level, fasting blood glucose level, total cholesterol level, and body mass index plus model 2. The prevalence of dental caries according to sleep duration showed a U-shaped curve of 33.4%, 29.4%, 28.4%, 29.4%, and 31.8% with ${\leq}5$, 6, 7, 8, and ${\geq}9$ hours of sleep, respectively. In the fully adjusted model 3, the risk of developing dental caries was significantly higher with ${\leq}5$ than with 7 hours of sleep (odds ratio, 1.23; 95% confidence interval, 1.06~1.43). The prevalence of periodontitis according to sleep duration showed a U-shaped curve of 34.4%, 28.6%, 28.1%, 31.3%, and 32.5%, respectively. The risk of periodontitis was significantly higher with ${\geq}9$ than with 7 hours of sleep in models 1 and 2, whereas the significant association disappeared in model 3. In a nationally representative sample, sleep duration was significantly associated with dental caries formation and weakly associated with periodontitis. Adequate sleep is required to prevent oral diseases such as dental caries and periodontitis.

• Korean Journal of Psychosomatic Medicine
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• v.22 no.1
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• pp.13-22
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• 2014

Objectives : Breast cancer has been the most prevalent female cancer in South Korea since 2001. Early detection of this disease is the most effective strategy for reducing mortality. The objective of this study was to identify factors which could predict advanced stage at diagnosis of breast cancer. Methods : Participants who were initially diagnosed with breast cancer and referred to the Stress Clinic of the Breast Cancer Center at Kyungpook National University Hospital were included. Through a semi-structured interview, the authors investigated psychosocial variables such as the extent of marital and family functioning and emotional-economic family burden as well as sociodemographic and health behavior-, health characteristic- and cancer-related variables. Results : Data were collected from 219 participants. One hundred and twenty(54.8%) subjects were diagnosed with advanced-stage breast cancer. Variables that were significantly different between the advanced-stage and early-stage groups included : monthly breast self examination(p<0.000), annual mammographic screening(p<0.000), mode of tumor detection(p<0.000), nature of the first symptoms(p<0.000), time to treatment after diagnosis(p<0.000), overloaded economic and family burden(p=0.018), marital functioning(p<0.000) and family functioning(p<0.00). Logistic regression analysis indicated that irregular annual mammography screening(OR=7.431 ; 95% CI 2.407-22.944) or a lack of screening(OR=25.299 ; 95% CI 7.855-81.482) and a dysfunctional marital relationship(OR=4.772 ; 95% CI 2.244-10.145) were significantly associated with advanced stage at diagnosis of breast cancer. Conclusions : We reconfirmed screening behavior to be a risk factor for delayed diagnosis of breast cancer. Our findings also emphasized the importance of psychosocial factors such as marital functioning in early detection of breast cancer. Psychiatric consultation in the area of martial functioning could be beneficial for increasing early detection in breast cancer.