• Journal of Nutrition and Health
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• v.42 no.6
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• pp.577-595
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• 2009

This study aimed to reanalyze energy and nutrient intakes of 2007 Korean Nutrition and Health Examination Survey (KNHANES) using CAN-Pro 3.0, a commonly used nutrient analysis software in Korea. Food items and their codes were selected from 2007 KNHANES dietary intake file and converted to food codes of CAN-Pro 3.0 nutrient database (NDB). Of the 1,324 total food items, 1,155 items were converted by direct matching, 123 items were matched using other items in CAN-Pro 3.0 NDB and 42 items were matched using external sources. Consumption frequencies of items converted by direct matching contributed 94.5% of total consumption. Nutrient intakes of 4,091 participants of 2007 KNHANES, over 1 year old, were recalculated using CAN-Pro 3.0 NDB and compared with intakes in 2007 KNHANES dietary intake file. Intakes for energy and all nutrients except protein and Vitamin C calculated by two NDBs were significantly different by paired t-test (p < 0.001), but significantly correlated by Pearson' correlation coefficients (p < 0.001). Percent differences between the NDBs ranged from 0.3% to 15.1%, low for protein, energy, vitamin C, iron, vitamin B$_2$ (below 5%) but high for phosphorus, retinol, vitamin A, and $\beta$-carotene (over 10%). Age group, sex, and their interactions significantly influenced six nutrients (p < 0.05). Intake levels of zinc, vitamin B6, vitamin E, folate and cholesterol were not available in 2007 KNHANES but were calculated by CAN-Pro 3.0. Mean intake levels of zinc, vitamin B$_6$, vitamin E, and folate by age and sex groups revealed that some groups had mean levels below RI (Recommended Intake) or AI (Adequate Intake) levels. Intake level of cholesterol was higher than the recommended level (below 300 mg/day) in some groups, especially males. Results of the present study indicate the need for comparable and more comprehensive NDB to be used for dietary assessment of KNHANES and other researches. More rigorous evaluation of nutrients which have not been reported in KNHANES is needed.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Preventive Medicine and Public Health
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• v.30 no.3 s.58
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• pp.585-598
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• 1997

Background : Although there are growing concerns about the adverse health effect of air pollution, not much evidence on health effect of current air pollution level had been accumulated yet in Korea. This study was designed to evaluate the chronic health effect of ai. pollution using Korean Medical Insurance Corporation (KMIC) data and air quality data. Medical insurance data in Korea have some drawback in accuracy, but they do have some strength especially in their national coverage, in having unified ID system and individual information which enables various data linkage and chronic health effect study. Method : This study utilized the data of Korean Environmental Surveillance System Study (Surveillance Study), which consist of asthma, acute bronchitis, chronic obstructive pulmonary diseases (COPD), cardiovascular diseases (congestive heart failure and ischemic heart disease), all cancers, accidents and congenital anomaly, i. e., mainly potential environmental diseases. We reconstructed a nested case-control study wit5h Surveillance Study data and air pollution data in Korea. Among 1,037,210 insured who completed? questionnaire and physical examination in 1992, disease free (for chronic respiratory disease and cancer) persons, between the age of 35-64 with smoking status information were selected to reconstruct cohort of 564,991 persons. The cohort was followed-up to 1995 (1992-5) and the subjects who had the diseases in Surveillance Study were selected. Finally, the patients, with address information and available air pollution data, left to be 'final subjects' Cases were defined to all lung cancer cases (424) and COPD admission cases (89), while control groups are determined to all other patients than two case groups among 'final subjects'. That is, cases are putative chronic environmental diseases, while controls are mainly acute environmental diseases. for exposure, Air quality data in 73 monitoring sites between 1991 - 1993 were analyzed to surrogate air pollution exposure level of located areas (58 areas). Five major air pollutants data, TSP, $O_3,\;SO_2$, CO, NOx was available and the area means were applied to the residents of the local area. 3-year arithmetic mean value, the counts of days violating both long-term and shot-term standards during the period were used as indices of exposure. Multiple logistic regression model was applied. All analyses were performed adjusting for current and past smoking history, age, gender. Results : Plain arithmetic means of pollutants level did not succeed in revealing any relation to the risk of lung cancer or COPD, while the cumulative counts of non-at-tainment days did. All pollutants indices failed to show significant positive findings with COPD excess. Lung cancer risks were significantly and consistently associated with the increase of $O_3$ and CO exceedance counts (to corrected error level -0.017) and less strongly and consistently with $SO_2$ and TSP. $SO_2$ and TSP showed weaker and less consistent relationship. $O_3$ and CO were estimated to increase the risks of lung cancer by 2.04 and 1.46 respectively, the maximal probable risks, derived from comparing more polluted area (95%) with cleaner area (5%). Conclusions : Although not decisive due to potential misclassication of exposure, these results wert drawn by relatively conservative interpretation, and could be used as an evidence of chronic health effect especially for lung cancer. $O_3$ might be a candidate for promoter of lung cancer, while CO should be considered as surrogated measure of motor vehicle emissions. The control selection in this study could have been less appropriate for COPD, and further evaluation with another setting might be necessary.