• 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 Nutrition and Health
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• v.47 no.4
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• pp.247-257
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• 2014

Purpose: It is reported that most senior people consume a high carbohydrate diet, while a high carbohydrate diet could contribute to the risk of chronic disease. The aim of this study is to determine whether a high carbohydrate diet can increase the risk of chronic disease in elderly Koreans. Methods: Using the 2007-2009 Korean National Health Nutrition Examination Survey data, out of a total of 3,917 individuals aged 65 and above, final 1,535 subjects were analyzed, divided by dietary carbohydrate energy ratio into two groups of moderate carbohydrate ratio (MCR, 55-70%) and excessive carbohydrate ratio (ECR, > 70%). All data were processed after the application of weighted value, using a general linear model or logistic regression. Results: Eighty one percent of elderly Koreans consumed diets with carbohydrate energy ratio above 70%. The ECR group included more female subjects, rural residents, lower income, and lower education level. The ECR group showed lower waist circumference, lower diastolic blood pressure, and lower frequency of consumption of meat and egg, milk, and alcohol. The intake of energy and most nutrients, with the exception of fiber, potassium, vitamin A, and carotene, was lower in the ECR group compared to the MCR group. When analyzed by gender, the ECR group showed lower risk of dyslipidemia in male and obesity in female subjects, even though the ECR group showed low intake of some nutrients. No difference in the risk of hypertension, diabetes, and anemia was observed between the two groups in male or female subjects. Conclusion: This result suggested that a high carbohydrate diet would not be a cause to increase the risk of chronic disease in the elderly. Further study is needed in order to determine an appropriate carbohydrate energy ratio for elderly Koreans to reduce the risk of chronic disease.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.