• Korean Journal of Adult Nursing
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• v.16 no.2
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• pp.327-336
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• 2004

Purpose: The aim of this study was to identify the age-related differences in risk perception of breast cancer and the age-related differences in predictors of risk perception. Method: The subjects were 553 women aged from 15 to 64 who lived in Seoul, Kwang-Ju, Jeon-nam province. Data was collected by self-reported questionnaire surveys using convenience sampling. The collected data was analyzed using descriptive statistics, F-test with Scheffe test, and stepwise multiple regression with SPSS-Win 10.0 version. Result: Risk perception of breast cancer was different by age group. Especially, risk perception in teenagers and the 50~64 years group was lower than the other groups. Additionally, general fear of breast cancer, information seeking style, experience, and knowledge were different between the age groups. The results of multiple regression analyses predicting risk perception indicated the following significant predictors: general fear and knowledge in all the subjects, general fear and experience in the 20~29 years group, and general fear in the other age groups. Conclusion: These results suggested that the construction of educational messages for breast cancer prevention and early detection should be designed differently for each age group.

• The Korean Journal of Health Service Management
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• v.11 no.4
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• pp.115-132
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• 2017

Objectives : The purpose of this study was to investigate the depressive change and predictors of chronic illnesses and to examine whether depressive change predictors are different according to chronic disease types. Methods : Data from the 1st to the 10th year of the Korean Welfare Panel of 5,023 adults over 20 years of age who were treated for chronic diseases for more than three months were examined. We assessed demographic, sociological, and socioeconomic factors including depression predictors of chronically ill patients, and analyzed the data using latent growth modeling and multiple group analysis. Results : Depression symptoms of chronically ill patients gradually decreased, showing a linear relationship across time. In addition, multiple group analysis was performed for each type of chronic disease. Conclusions : We described the phenomenon of depression in chronically ill patients, and discussed the practical and policy interventions required to improve depression in chronically ill patients.

• Korean Journal of Adult Nursing
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• v.17 no.1
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• pp.23-33
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• 2005

Purpose: To evaluate physical characteristics, lifestyle related to bone-health, and bone mineral density (BMD) in mothers and their daughters and to determine the predictors of BMD. Method: BMDs at the forearm, lumbar spine, and femur were measured in 101 healthy, mother-daughter pairs by dual energy X-ray absorptiometry. Mother-daughter differences between general characteristics, means for BMDs were assessed by ${\chi}^2$-test, t-tests. Multiple regression analyses were used to identify predictors of BMD in each group. Results: Mothers had significantly higher BMD than their daughters at forearm, lumbar spine, and femur. The predictors of mothers' BMDs were body weight, body mass index (BMI) and percentage body fat, explaining 5.1~31.6% of the variation in BMDs. BMI, percentage body fat and their mother's BMD of the corresponding site bone were predictors in daughters, explaining 17.5~31.6% of the variations in BMDs. Conclusion: These results indicate the importance of weight on bone that the BMDs seems to be related to fat free mass both in young-adult daughters and in middle aged mothers. These also suggest the importance ofintervention for the development of BMD in daughter of mother with low BMD.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.3
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• pp.160-166
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• 1989

We propose adaptive algorithms computing the coefficients of spatial domain predictors. The method uses the LMS approach to compute the coefficients of the predictors realized by using the TDL(tapped-delay-line) and the ESC (escalator) structures. The predictors to be presented differ from the conventional ones in the sense that the relevant weights are updated such that the sum of the mean squared values of the forward and the backward prediction errors is minimized. Using the coefficients of such spatial domain predictors yields improved linear predictive spatial spectrums. The algorithms are applied to the problems of estimating incident angles of multiple narrow-band signals received by a linear array of sensors. Simulation results demonstrating the performances of the proposed methods are presented.

• Women's Health Nursing
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• v.1 no.2
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• pp.244-259
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• 1995

This study was designed and carried out to describe the stress of the infertile women and identify its predictors. The subjects of this study, 131 infertile women, were in primary or secondary infertility. the were conveniently sampled out from the infertility clinics of K University Medical Center and C Hospital in Seoul. The data were collected by using the Infertility Stress Scale which consisted of 35 items with four dimensions(cognitive, affective, marital and social stress) from August to November 1994. The data were analyzed by using the pc-SAS program. The information was obtained of Mean, Standard Deviation, Frequencies, Percentile, t-test, ANOVA, Duncan's multiple comparison test and Multiple Regression. The results are as follows; 1. The Mean of the stress of the infertile women is 2.78. The Means of the stress in 4 dimensions are 3.81 in the cognitive dimension, 3.05 in the affective dimension, 2.06 in the marital adjustment dimension and 2.41 in the social adjustment dimension. 2. The predictors of the stress of the infertile women are their educational levels and subjective economic status. They explain 14.08% of total variance.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• The Journal of Korean Society for School & Community Health Education
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• v.14 no.3
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• pp.51-65
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• 2013

Objectives: The main purpose of this study was to compare influencing factors on depression between male and female students at the high school. Methods: The self-administered questionnaire survey was carried out on a convenience sample of 403 high school students. The data analysis procedure included frequency, ${\chi}^2$ test, t-test, ANOVA, Pearson correlation coefficients, and stepwise multiple regression using depression as the dependent variable. Results: There was a significant gender difference in depressive symptoms; the mean depression score of female students was higher than that of male students. Stepwise multiple regression analysis for depression revealed that the most powerful predictors (34%) were powerlessness and self-esteem for male students. On the other hand, the factors such as self-esteem, mother's occupation, and family fucntion were the most significant predictors (50%) for female students. Conclusion: The necessity of an intervention considering gender difference in high school students so as to prevent the occurrence of depression was suggested.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1396-1403
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• 2011

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

• Journal of Society of Preventive Korean Medicine
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• v.21 no.2
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• pp.35-44
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• 2017

Objectives : The purpose of this study was to investigate the association between quality of life and Mibyeong index of abnormal symptoms by the Mibyeong state. Methods : A total of 1,100 people were recruited into this study and were collected by specialized research company. The data were collected by personal information using MBI(Mibyeong Index) and SF-12 (Short form-12) and were analyzed with SPSS (version 21.0) computer program, and included Pearson's chi-square test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis. Results : The Mibyeong Index which represents abnormal symptom type showed significant difference for Mibyeong state (p<.001). In Total subject, fatigue, pain, low sleep quality and indigestion were found to be significant predictors of PCS(Physical Component Summary). In Healthy group, pain was found to be significant predictor of PCS. In MI 1 group, fatigue, pain, indigestion and anxiety were found to be significant predictors of PCS. In MI 2 group, fatigue, pain and low sleep quality were found to be significant predictors of PCS. In Total subject, fatigue, depression, anger and anxiety were found to be significant predictors of MCS (Mental Component Summary). In Healthy group, indigestion and anxiety were found to be significant predictors of MCS. In MI 1 group, depression and anger were found to be significant predictors of MCS. In MI 2 group, fatigue and depression were found to be significant predictors of MCS. Conclusions : The relationship between Quality of life and MBI changes according to Mibyeong state. We expect that this result could be contribute to health management of people in Mibyeong state.

• Journal of Preventive Medicine and Public Health
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• v.46 no.6
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• pp.300-308
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• 2013

Objectives: The purpose of this study was to classify determinants of cost increases into two categories, negotiable factors and non-negotiable factors, in order to identify the determinants of health care expenditure increases and to clarify the contribution of associated factors selected based on a literature review. Methods: The data in this analysis was from the statistical yearbooks of National Health Insurance Service, the Economic Index from Statistics Korea and regional statistical yearbooks. The unit of analysis was the annual growth rate of variables of 16 cities and provinces from 2003 to 2010. First, multiple regression was used to identify the determinants of health care expenditures. We then used hierarchical multiple regression to calculate the contribution of associated factors. The changes of coefficients ($R^2$) of predictors, which were entered into this analysis step by step based on the empirical evidence of the investigator could explain the contribution of predictors to increased medical cost. Results: Health spending was mainly associated with the proportion of the elderly population, but the Medicare Economic Index (MEI) showed an inverse association. The contribution of predictors was as follows: the proportion of elderly in the population (22.4%), gross domestic product (GDP) per capita (4.5%), MEI (-12%), and other predictors (less than 1%). Conclusions: As Baby Boomers enter retirement, an increasing proportion of the population aged 65 and over and the GDP will continue to increase, thus accelerating the inflation of health care expenditures and precipitating a crisis in the health insurance system. Policy makers should consider providing comprehensive health services by an accountable care organization to achieve cost savings while ensuring high-quality care.