• The Korean Journal of Pain
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• v.33 no.2
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• pp.153-165
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• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.28-33
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• 2022

This study aims to investigate factors that affect elderly poverty based on a comprehensive and universal perspective, suggesting some alternatives for improving the poverty rate of the elderly. The comprehensive and universal approach to the poverty of the aged that this study attempts can give a better understanding of the elderly poverty beyond the contribution of the existing literature, with the research model including individual, family, labor, and income factors as the causes of old-age poverty from the comprehensive and universal perspective on the causes of poverty of the elderly. In addition, the study attempts to input variants of variables into the equation for the causes of elderly poverty by using panel data from the 8th Korean Retirement and Income Study. This study employs decision tree analysis to determine the cause of the poverty of the elderly using CHAID. The decision tree analysis shows that the most vital variable affecting elderly poverty is making income. For the poor elderly without earned income, public pensions, educational careers, and residential areas influence elderly poverty, but for the poor elderly with earned income, wage earners and gender are variables that affect poverty. This study suggests some alternatives to improve the poverty rate of the aged. The government should create a better working environment such as senior re-employment for old people to be able to participate in economic activities, improve public pension or social security for workers with unfavorable conditions for public security of old age, and give companies that create employment of the aged diverse incentives.

• Research in Community and Public Health Nursing
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• v.14 no.4
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• pp.699-706
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• 2003

The objective of this study is to find out the difference in perceptional fatigue and health promoting behavior between hospital nurses and public health nurses. The subjects of this study were 141 hospital nurses and 73 public health nurses in Daejeon. Data were collected using a self-reporting questionnaire during the period from the 5th to 16th of March 2003. Collected data were analyzed using SPSS program. Real number, percentage, mean and standard deviation were calculated, and $x^2$-test and t-test, ANOVA, Pearson's correlation coefficient, stepwise multiple regression procedures were carried out. The findings of this study as follows: 1. The mean score of health promoting behavior was 2.71. 2. There were statistically significant differences in health promoting behavior according to age, marital status, family status, residency, educational level, income, the length of work experience and the field of work. (p<0.05) 3. There were statistically significant differences in fatigue according to age, marital status, family status, educational level, income, the length of work experience, perceived health status and the field of work. (p<0.05) 4. The fatigue was found to be in significant negative correlations with health promoting (r=-0.358, p<0.000) and self efficacy (r=-0.314, p<0.000). On the contrary, a significant positive correlation was found between fatigue and perceived barriers (r=0.210, p<00.01). 5. There were five predictors affecting health promoting behavior, which were self-efficacy, income, perceived benefit, fatigue and family support. The most influential factor was self-efficacy that made 31% of prediction, followed by income (6%), perceived benefit (5.2%), fatigue (2.2%) and family support (1.7%) in their order. As a whole, these factors made 46.1% of prediction of health promotion behavior.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.248-253
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• 2019

Early diagnosis of mild cognitive impairment (MCI) can reduce the incidence of dementia. This study developed the MCI prediction model for the elderly in Korea. The subjects of this study were 3,240 elderly (1,502 men, 1,738 women) aged 65 and over who participated in the Korean Longitudinal Survey of Aging (KLoSA) in 2012. Outcome variables were defined as MCI prevalence. Explanatory variables were age, marital status, education level, income level, smoking, drinking, regular exercise more than once a week, average participation time of social activities, subjective health, hypertension, diabetes Respectively. The prediction model was developed using Restricted Boltzmann Machine (RBM) neural network. As a result, age, sex, final education, subjective health, marital status, income level, smoking, drinking, regular exercise were significant predictors of MCI prediction model of rural elderly people in Korea using RBM neural network. Based on these results, it is required to develop a customized dementia prevention program considering the characteristics of high risk group of MCI.

• The Journal of Fisheries Business Administration
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• v.15 no.1
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• pp.114-130
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• 1984

The structure of food demand is being changed according to the improvement of living standard. Moreover, the intake of animal protein is stepping up. This paper considers how much fresh-fish is consumed as source of animal protein and what extent fresh-fish have substitutive relation for meat with special reference to the change of income and price of fresh-fish and meat. And it is thought to be important work to estimate demand of fresh-fish in attemps to the prediction of food consume pattern and fishing industries in the future. For this estimation, the substitutive relation of fresh-fish and meat is essentially studied. The main conclusions of this study can be drawn as follows: 1. Fresh-fish and meat have substitutive relation on price axis. By the way, increase in demand of A (fresh-fish which have comparatively low price) can be expected according to the low of it's price against meat, but B (fresh-fish wihich have comparatively middle-high price) have peculiar demand without substitutive relation for meat. 2. Demand of A and B rise according to the income increases. 3. It is not sufficient to explain substutive relation of fresh-fish and meat without income variable. 4. Income increases bring about the more increase in demand of B than A. By the way, price increases bring about the decrease of it's consume expenditure, but A have fundamental demand as the source of animal protein. 5. In future, the intake of animal protein will step up. By the way, meat will occupy the more portion of the source of animal protein than fresh-fish.

• East Asian Journal of Business Economics (EAJBE)
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• v.7 no.1
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• pp.17-25
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• 2019

Purpose - The international market provides a growth momentum for firms by allowing them to tap into a new market. Given information asymmetry between firms and financial analysts, firms' international growth can be perceived as a higher business prospect by analysts. This paper explores the possibility of analysts' over-emphasis on foreign income growth in predicting earnings. Research design, data, and methodology - We utilize a sample of U.S. firms to test the relationship between foreign income growth and analysts' forecast optimism. Our sample of publicly listed and traded U.S. firms between 1976 and 2016 consists of 6,120 firm-year observations. Results - Empirical analyses show that firms that show higher international growth in earnings are likely to face forecast inaccuracy by financial analysts. From the perspective of firms, their earnings are less than what analysts forecasted. Contrary to our prediction on the moderating effect of innovative capabilities, optimistic bias is not intensified - rather, it is reduced - when firms have higher innovative capabilities. Conclusions - Our results imply that while analysts favor firms with higher international growth, innovative capability on the international market places additional risks to firms' operation.

• Journal of Korean Clinical Health Science
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• v.4 no.2
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• pp.582-592
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• 2016

Purpose. For more effective hospital management, it analyzes the trend through general characteristics, balance sheet, income statement, and financial ratio analysis, grasps the causes of the problems, and analyzes management of the hospital in order to use the result as baseline data for development of the hospital in the future. Methods. The collected data of 3 years from 2011 to 2013 about 3 tertiary hospitals in metropolitan cities from Alio (provider of public institution information; www.alio.go.kr), Health Insurance Review & Assessment Service (www.hira.or.kr), and the website of the Ministry of Health and Welfare (www.mw.go.kr) were analyzed and general characteristics, balance sheet, income statement, and financial ratio, analysis are used as data. Results & Conclusions. From the result of data analysis from 2011 to 2013, general characteristics, balance sheet, income statement, financial ratio analysis, and pie charts could lead to conclusions as follows. In the result of comprehensive analysis, the 3 tertiary hospitals showed increase of fixed expense due to extension of the buildings and so did the scale of fund and asset. Although medical revenue increased, the margin of increase for medical expense was greater than that of medical revenue, which consequently led to loss. In prediction for the 3 tertiary hospitals based on characteristics so far, it is expected to see improved revenue structure after building extension is completed, but it is necessary to exert management effort to maintain its optimal level by enhancement in stability of management and inventory turnover through management of inventories.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.31-40
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• 2020

This study investigates whether other comprehensive income (OCI) reported in the statement of comprehensive income (one of the main financial statements after the adoption of K-IFRS) predicts a firm's future performance. Using the quarterly data of Korean listed companies, we examine the association between OCI estimates and future earnings. First of all, we find that OCI is positively associated with earnings in both 1- and 2-quarter ahead, supporting the predictive value of OCI. When we break down OCI into its individual components, our results suggest that the net unrealized gains/losses on available-for-sale (AFS) investment securities are positively associated with future earnings, while the other components (e.g., net unrealized gains/losses on valuation of cash flow hedge derivatives) present insignificant results. In addition, we investigate whether the reliability in OCI estimates enhances the predictive value of OCI to predict future performance. We find that the predictive ability of OCI, in particular the net unrealized gains/losses on available-for-sale (AFS) investment securities, becomes more pronounced when firms are audited by the Big 4 audit firms. Overall, our study suggests that information content embedded in OCI can provide decision-useful information that is helpful for the prediction of future firm performance.

• Korean Journal of Health Education and Promotion
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• v.30 no.5
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• pp.139-151
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• 2013

Objectives: This study investigated how income, duration of illness, social stigma, quality of sleeping, ADL and social participation related to Parkinson's disease(PD) predict depression in a conceptual model based on the International Classification of Functioning(ICF) model. Methods: The sample included 206 adults with idiopathic Parkinson's disease(IPD) attending D university hospital in B Metro-politan City. A structured questionnaire was used and conducted face-to-face interviews. The collected data were analyzed for fitness, using the AMOS 18.0 program. Results: A path analysis showed that the overall model provided empirical evidence for linkages in the ICF model. Depression was manifested by significant direct effects of social stigma(${\beta}=.20$, p<.001), quality of sleeping(${\beta}=-.40$, p<.001), ADL(${\beta}=-.20$, p<.01), and social participation(${\beta}=-.12$, p<.05), indirect effects including income(p<.05), duration of illness(p<.05). These variables explained 45.9% of variance in the prediction model. Conclusions: This model may help nurses to collect and assess information to develop intervention program for depression.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.151-161
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• 2018

Income is a vital aspect of economic life. Knowing what their income will help people create budgets that allow them to pay for their living expenses. Income data is used by banks, stores, and service companies for marketing purposes and for retaining loyal customers; it is a crucial demographic element used at a wide variety of customer touch points. Therefore, it is essential to be able to make income predictions for existing and potential customers. This paper aims to predict employment earnings or income based on history, and uses machine learning techniques such as SVMs (Support Vector Machines), Gaussian, decision tree and DCNNs (Deep Convolutional Neural Networks) for predicting employment earnings. The results show that the DCNN method provides optimum results with 88% compared to other machine learning techniques used in this paper. Improvement of the data length such PCA has the potential to provide more optimum result.