• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.43-48
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• 2014

Purpose: In the PET/CT images, The SUV (standardized uptake value) enables the quantitative assessment according to the biological changes of organs as the index of distinction whether lesion is malignant or not. Therefore, It is too important to enter parameters correctly that affect to the SUV. The purpose of this study is to evaluate an allowable error range of SUV as measuring the difference of results according to input errors of Activity, Weight, uptake Time among the parameters. Materials and Methods: Three inserts, Hot, Teflon and Air, were situated in the 1994 NEMA Phantom. Phantom was filled with 27.3 MBq/mL of 18F-FDG. The ratio of hotspot area activity to background area activity was regulated as 4:1. After scanning, Image was re-reconstructed after incurring input errors in Activity, Weight, uptake Time parameters as ${\pm}5%$, 10%, 15%, 30%, 50% from original data. ROIs (region of interests) were set one in the each insert areas and four in the background areas. $SUV_{mean}$ and percentage differences were calculated and compared in each areas. Results: $SUV_{mean}$ of Hot. Teflon, Air and BKG (Background) areas of original images were 4.5, 0.02. 0.1 and 1.0. The min and max value of $SUV_{mean}$ according to change of Activity error were 3.0 and 9.0 in Hot, 0.01 and 0.04 in Teflon, 0.1 and 0.3 in Air, 0.6 and 2.0 in BKG areas. And percentage differences were equally from -33% to 100%. In case of Weight error showed $SUV_{mean}$ as 2.2 and 6.7 in Hot, 0.01 and 0.03 in Tefron, 0.09 and 0.28 in Air, 0.5 and 1.5 in BKG areas. And percentage differences were equally from -50% to 50% except Teflon area's percentage deference that was from -50% to 52%. In case of uptake Time error showed $SUV_{mean}$ as 3.8 and 5.3 in Hot, 0.01 and 0.02 in Teflon, 0.1 and 0.2 in Air, 0.8 and 1.2 in BKG areas. And percentage differences were equally from 17% to -14% in Hot and BKG areas. Teflon area's percentage difference was from -50% to 52% and Air area's one was from -12% to 20%. Conclusion: As shown in the results, It was applied within ${\pm}5%$ of Activity and Weight errors if the allowable error range was configured within 5%. So, The calibration of dose calibrator and weighing machine has to conduct within ${\pm}5%$ error range because they can affect to Activity and Weight rates. In case of Time error, it showed separate error ranges according to the type of inserts. It showed within 5% error when Hot and BKG areas error were within ${\pm}15%$. So we have to consider each time errors if we use more than two clocks included scanner's one during the examinations.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.7 no.1
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• pp.5-28
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• 2001

Objectives: The objective of this study was to investigate analysis of bone mineral density according to Women with low back pain women. Methods: The data were collected from women who visited Physical Examination Center of a Catholic university hospital located in Daegu. Questionnaires were completed by 50 women during the period from July 20, 2000 to January 12, 2001. The sample was divided into three groups(the normal group of 16 cases and the osteopenia group of 12cases and the osteoporosis group of 22 cases). Bone mineral density(BMD) of lumbar spine was measured using energy absorptiometry. Results: The bone mineral density of the lumbar spine decreased with aging. The bone mineral density of the lumbar spine decreased with the serum Calcium and Phosphorus and Alkaline phosphatase increased. The mean bone mineral density of the lumbar spine of healthy women in age(50~59) was 0.87g/$cm^2$, the lumbar spine of women with low back pain in age(50~59) was 0.77g/$cm^2$. In the multiple regression of risk factors to bone mineral density(BMD) of lumbar spine were correlated with age, marriage existence, exercise time, the loving food of taste, calcium, bone mineral density standard T scores(p<0.05). The experience for LBP increased as weight increased(Odds ratio=999.000). The experience for LBP increased as number of Exercise decreased(Odds ratio=999.000). The experience for LBP increased as menopause existence increased(Odds ratio=999.000). The experience for LBP increased as serum Calcium and Phosphorus increased (Odds ratio=999.000). however all four variables had significant no relationship. The correlation in variables in relation to low back pain and bone mineral density, age showed contra-correlation with low back pain existence, Alkaline phosphatase(p<0.01). Weight showed contra-correlation with body mass index(BMI)(p<0.01). Exercise time showed correlation with number of exercise(p<0.01). The loving food of taste showed contra-correlation with Alkaline phosphatase(p<0.05). Bone mineral density showed correlation with menopause existence(p<0.05). Conclusions: Results from this study indicated that a statistically significant association between bone mineral density of the lumbar spin and age, marriage existence, exercise time, the loving food of taste, calcium, bone mineral density standard T scores. In logistic regression test, there were no related variables. The combination of bone mineral density measurement and assessment of the bone turnover rate by measuring biochemical would be helpful for the treatment of patients with risks of osteoporosis. The more precise study for risk factors to osteoporosis is essential.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Nutrition and Health
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• v.35 no.3
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• pp.352-367
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• 2002

In order to assess the quality of dietary intake among the elderly, a survey was conducted during Jucy-August, 1999, of 230 subjects who were 65 years or older and who were living in Jeonju City. Results of the analysis of the data are as follows : Regarding Dietery Variety Score (DVS), the average number of food items consumed per person was significantly higher for males (19.6) than for females (17.7). The intake of plant food was higher than animal food for both sexes the proportion of plant versus animal foods consumed by fresh weight was 85 : 15 for males and 89 : 11 for females. Diet Diversity Score (DDS) is determined by how many from five food groups (cereal, meat, dairy, vegetable and fruit) consumed per day while Korean Diet Diversity Score (KDDS) is determined by how many from five different food groups (cereal, meat, vegetable, dairy and oil) consumed per day. The subjects'average DDS and KDDS were 4.0 and 3.5 for males, and 3.7 and 3.2 for females, respectively. Overall, the distribution of DDS was lower than that of KDDS. The average Meal Balance Score (MBS : Apply the KDDS at breakfast, lunch and dinner) was 9.1 for malts and 8.1 for females. Average daily caloric intake for males and females was 1,740 kcal and 1,433 kcal, which was 84.0% and 80.9% of the RDA, respectively. Average daily protein intake for males and females, at 67 g and 49 g (100.7% and 88.3% of the RDA), respectively, was satisfactory. However, intakes of calcium and vitamin A were below 75% of the RDA (calcium : 62.7% for males and 55.3% for females ; vitamin A : 60.7% for males and 53.9% far females). The average proportional contribution of protein/fat/carbohydrate (PFC) to total calorie intake was 15.8 : 15.7 : 68.5 for males and 13.8 : 13.2 : 73.0 for females. Distribution of energy for each meal (breakfast : lunch : afternoon snack : dinner : night snack) was 29.2 : 32.4 : 5.0 : 31.2 : 2.2 among males and 30.5 : 33.5 : 4.5 : 28.6 : 2.91 among females. The Index of Nutritional Quality (INQ) was above 1 for protein, phosphorus, iron, vitamin B$_1$, niacin, and vitamin C. However, the INQ of calcium and vitamin A were below 1 among both males and females, and the INQ of vitamin B$_2$was below l among females. The Nutrient Adequacy Ratio (NAR = nutrient intake %RDA) was below 1 for all nutrients, and the NAR of vitamin A were the lowest among 9 nutrients (protein, calcium, phosphorus, iron, vitamin A, vitamin B$_1$, vitamin B$_2$, niacin, vitamin C) for both males and females, with values of 0.52 and 0.42, respectively. The second and third lowest NAR values were for calcium(males: 0.68: females: 0.54) and vitamin B$_2$(males: 0.77: females: 0.67). Values of Mean Adequacy Ratio (MAR = sum of 9 NARs/9) for males (0.82) were higher than for females (0.73). These results indicate that the intakes of calcium and vitamin A were severely inadequate. The results of a stepwise multiple regression analysis, where the DVS or MAR were the dependent variables and the DDS, KDDS, and MBS were independent variables, indicated that DDS is a more useful variable than KDDS in determining the quality of meals of the elderly.