• Journal of Hospice and Palliative Care
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• v.19 no.1
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• pp.61-69
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• 2016

Purpose: It is important to alleviate care burden for terminal cancer patients and their families. This study investigated the factors associated with care burden among family caregivers (FCs) of terminally ill cancer patients. Methods: We analyzed data from 289 FCs of terminal cancer patients who were admitted to palliative care units of seven medical centers in Korea. Care burden was assessed using the Korean version of Caregiver Reaction Assessment (CRA) scale which comprises five domains. A multivariate logistic regression model with stepwise variable selection was used to identify factors associated with care burden. Results: Diverse associating factors were identified in each CRA domain. Emotional factors had broad influence on care burden. FCs with emotional distress were more likely to experience changes to their daily routine (adjusted odds ratio (aOR), 2.54; 95% confidence interval (CI), 1.29~5.02), lack of family support (aOR, 2.27; 95% CI, 1.04~4.97) and health issues (aOR, 5.44; 2.50~11.88). Family functionality clearly reflected a lack of support, and severe family dysfunction was linked to financial issues as well. FCs without religion or comorbid conditions felt more burdened. The caregiving duration and daily caregiving hours significantly predicted FCs' lifestyle changes and physical burden. FCs who were employed, had weak social support or could not visit frequently, had a low self-esteem. Conclusion: This study indicates that it is helpful to understand FCs' emotional status and family functions to assess their care burden. Thus, efforts are needed to lessen their financial burden through social support systems.

• Journal of Environmental Health Sciences
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• v.46 no.3
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• pp.285-296
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• 2020

Objective: The objective of this study was to describe the characteristics of exposure to humidifier disinfectants (HDs) and their association with the presence of a person who experienced the adverse health effects in general households in Korea. Methods: During the month of December 2016, a nationwide online survey was conducted on adults over 20 years of age who had experience of using HDs. It provided information on exposure characteristics and the experience of health effects. The final survey respondents consisted of 1,555 people who provided information on themselves and their household members during the use of HD. Exposure characteristics at the household level included average days of HD use per week, average hours of HD use per day, the duration within which one bottle of HD was emptied, average input frequency of HD, amount of HD (cc) per one time used, and active ingredients of HD products (PHMG, CMIT/MIT, PGH, or others). The risk of the presence of a person who experienced adverse health effects in the household was evaluated by estimating odds ratios (ORs) and 95% confidence intervals (CIs) adjusted for monthly income and region using a multiple logistic regression model. Subgroup analyses were conducted for households with a child (≤7 years) and households with a newborn infant during HD use. Results: The level of exposure to HD tended to be higher for households with a child or newborn infant for several variables including average days of HD use per week (P<0.0001) and average hours of HD use per day (P<0.0001). The proportion of households in which there was at least one person who experienced adverse health effects such as rhinitis, asthma, pneumonia, atopy/skin disease, etc. was 20.6% for all households, 25.3% for households with children, and 29.9% for households with newborn infants. The presence of a person who experienced adverse health effects in the household was significantly associated with average hours of HD use per day (P_trend<0.001), duration within which one bottle of HD was emptied (P_trend<0.001), average input frequency of HD (P_trend<0.001), amount of HD per one use (P_trend=0.01), and use of HDs containing PHMG (OR=2.23, 95% CI=1.45-3.43). Similar results were observed in subgroup analyses. Conclusion: Our results suggest that level of exposure to HD tended to be higher for households with a child or newborn infant and that exposure to HD is significantly associated with the presence of a person who experienced adverse health effects in the household.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Korean Home Economics Education Association
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• v.30 no.4
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• pp.1-14
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• 2018

The purpose of this study was to examine the relevance of mental health and nutritional intake according to the sleep duration of Korean adolescents. This study was based on data from the 2007-2016 Korea National Health and Nutrition Examination Survey(KNHNES), including 5,489 total subjects (2,795 middle school students, 2,694 high school students). The association between sleep duration and mental health was analyzed using a logistic regression analysis, and the link between sleep duration and nutritional intake was analyzed via a generalized linear model. An analysis of sleep duration showed that middle school students had a higher average sleep duration than high school students (P<0.0001). An analysis of the relationship between sleep duration and mental health showed that middle school students had lower rates of stress perception (P<0.0001) and suicidal ideation (P=0.0005) as their sleep duration increased. High school students had 53% less suicidal ideation in the group getting 6-7 hours compared to the group getting less than 6 hours, and 37% less suicidal ideation than the group getting 7-8 hours. The link between sleep duration and stress perception was statistically significant among both middle and high school students (P for interaction=0.02). An analysis of the daily intake of major nutrients according to sleep hours found high intake of vitamin C in groups where high school students slept more than nine hours (P=0.003). The state of nutritional intake according to higher sleep duration showed statistically significant differences between higher intake of phosphorus, riboflavin, niacin, and vitamin C in Nutrient Adequacy Ratio for high school students. In conclusion, adolescents' sleep duration is associated with stress perception, suicidal ideation and nutritional intake. Therefore, this study emphasizes the mental importance of adolescent sleep and can be used as a basis for nutrition education.

• Clinical and Experimental Reproductive Medicine
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• v.33 no.3
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• pp.159-170
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• 2006

Objective: The purpose of this study is to find out risk factors related to uterine leiomyoma in Korean women and to compare with the results of previous western studies. Methods: A retrospective analysis was carried out. All the cases of uterine leiomyoma (n=244) were diagnosed surgically or sonographically between Jannuary 1998 and December 2004. Total of 269 controls not having uterine leiomyoma were collected from patients who visited Kyungpook national university hospital for routine gynecologic check-up or treatment of their gynecologic or obstetric diseases other than uterine leiomyoma. Data were collected through review of medical records and interviews and analyzed with $x^2$ and logistic regression model. Results: In multivariate analysis, patient's age (OR 1.070; 95% CI 1.041~1.099), number of artificial abortion (OR 1.182; 95% CI 1.018~1.374) and alcohol drinking (OR 1.865; 95% CI 1.231~2.824) had significantly positive correlation with uterine leiomyoma. The duration of lactation was the only factor which had negative correlation (OR 0.985; 95% CI 0.972~0.998). BMI, parity, age at menarche, the duration and interval of menstruation, caffeine consumption and marital status did not show any correlations. Conclusion: In this study, patient's age, number of artificial abortion, and alcohol drinking were the risk factors of uterine leiomyoma in Korean women and the result was similar to that of western studies. Though we couldn't find out the specific risk factors related to the development of uterine leiomyoma in this study, but it has a great meaning to be the first trial in Korean women. The role of information bias should be carefully evaluated and further multicentered, randomized, controlled prospective studies will be needed to know the possible risk factors among Korean women.

• Journal of Preventive Medicine and Public Health
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• v.29 no.3 s.54
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• pp.669-678
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• 1996

Helicobacter pylori is now recognized as causative agent of chronic gastritis and peptic ulcer, and strongly associated with development of gastric carcinoma. With development of sensitive and specific serologic tests to identify individuals infected with Helicobacter pylori, the epidemiologic study of this diseases has been investigated. But it's transmission route is not established, yet. The purpose of this study is to measure the prevalence of Helicobacter pylori infection in healthy children and young adults and to evaluate related factors for Helicobacter pylori infection in Korea. The seroprevalence of Ig G antibodies to Helicobacter pylori was determined using a Enzyme Linked Immunosorbent Assay and we obtained the information, such as demographic characteristics, monthly household income, numbers of family members in the house, numbers of persons using same room, type of house, and type of drinking water through the questionnaire survey. The observed overall seropositivity rate was 25.7%. The rate is increased progressively from 5.8% in the age group $1\sim3$ years to 44.4% in the age group $20\sim29$years($\chi^2$ for trend, p<0.001). Especially, the rate increased steeply from 6.5% in the age group $4\sim6$ years to 20.8% in the age group $7\sim9$ years, and this suggested that elementary school age was the major acquisition time of Helicobacter pylori infection. In multivariate logistic regression model, age, numbers of family members in the house, and type of house was statistically significant variables for Helicobacter pylori infection. Each odds ratio(93% CI) were as follows; base to age group $1\sim9$ years, age group $10\sim19$ years $3.6(2.0\sim6.4)$, age group $20\sim29$ years $7.3(4.1\sim13.1)$ and base to group of $1\sim3$ family members, group of $4\sim5$ family members $2.1(1.1\sim4.0)$, group of 6 or more family members $2.7(1.3\sim5.4)$ and base to apartment, single and multihouse $1.9(1.1\sim3.5)$. Sex, monthly household income, numbers of persons using same room, and type of drinking water was not statistically significant for Helicobacter pylori infection.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of dental hygiene science
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• v.16 no.3
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• pp.242-248
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• 2016

The purposes of this study were to investigate the factors affection the Oral Impacts on Daily Performances for Children (C-OIDP) in elementary and middle school students, and identify the association between oral health-related behaviors, oral health condition and C-OIDP. A cross-sectional study was conducted in three schools in Incheon, Asan, Korea. A total of 175 selected children were interviewed by a trained examiner using a questionnaire. Oral Health Related Quality of Life was assessed by the Korean version of C-OIDP. Socio-economic characteristics, oral health-related behaviors, oral health condition and C-OIDP were verified using the questionnaire. ANOVA analysis was performed to determine the oral health and C-OIDP, and multiple regression analysis was performed to determine the factors affecting the C-OIDP. The activities with the greatest effect were eating (28.0%), cleaning teeth (22.9%), and smiling (18.9%). In the logistic regression model, the high item score of C-OIDP was associated with experiencing dental caries and gum pain in the past month. The more the C-OIDP prevalence item, the more the fillng deciduous tooth surface (fs) (p=0.024), caries experienced deciduous tooth surface (dfs) (p=0.049), total caries tooth surface (ds+DS) (p=0.021), and total caries experienced tooth surface (dfs+DMFS) (p=0.047). It can be concluded that the factors affecting C-OIDP are fs, dfs, dfs+DMFS, and gingival pain. Based on these results, we can improve C-OIDP to advance preventive practice.

• Journal of Genetic Medicine
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• v.5 no.1
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• pp.34-40
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• 2008

Purpose : Preeclampsia is a major cause of maternal and perinatal mortality and morbidity and is considered to be a multifactorial disorder involving a genetic predisposition and environmental factors. Endothelin-1 (ET-1) is a potent vasoconstrictor peptide, and alterations in the ET-1 system are thought to play a role in triggering the vasoconstriction seen with preeclampsia. The aim of this study was to examine the frequency of the 4 common single-nucleotide polymorphisms (SNPs) (c.1370T>G, c.137_139delinsA, c.3539+2T>C, and c.5665G>T) of the ET-1 gene in normotensive and preeclamptic pregnancies and to investigate whether these SNPs are associated with preeclampsia in pregnant Korean women. Methods : We analyzed blood samples from 206 preeclamptic and 216 normotensive pregnancies using a commercially available SNapShot kit and an ABI Prism 3100 Genetic analyzer. Results : There were no significant differences in genotype or allele frequencies of the 4 SNPs in the ET-1 gene between preeclamptic and normotensive pregnancies. The respective frequencies of the 3 haplotypes (TDTG, GDCT, and TICT; >10% haplotype frequency) were 61%, 13% and 13%, respectively, in preeclampsic pregnancies and 62%, 14% and 12%, respectively, in normotensive pregnancies. The frequencies of these haplotypes were similar for both groups. Using multiple logistic regression analysis, we did not observe an increase in the risk of preeclampsia for the 4 SNPs of the ET-1 gene under either a recessive or dominant model. Conclusion : This study suggests that the 4 SNPs of the ET-1 gene are not associated with an increased risk for preeclampsia in pregnant Korean women.

• Journal of Preventive Medicine and Public Health
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• v.29 no.2 s.53
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• pp.199-214
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• 1996

To investigate the factors which affecting the cognitive function and depression of the 65 or more age group, the authors surveyed for the subjects in the region of Taejon and nearby Taejon area. 729 studied subjects were tested for cognitive function with MMSE and depression with GDS. The main results were followings; In the studied subjects, the rate of normal cognitive function was 56.8%, the rate of mildly impaired was 24.1% and the rate of severe impairment was 19.1%. The cognitive function level was closely related to the depression score. As the age increased, the cognitive function was more impaired. Sexual difference was also existed in the cognitive function level and the depression score. After adjusting the effect of age, the variables such as sex, marital status, education level, past job, instrumental ability of daily living, regular physical exercise, frequencies of going out the house, chest discomfort, visual and auditory disturbance, and dizziness had the significant relationship with cognitive function impairment. Among these variables instrumental ADL, age, visual disturbance, and sex showed statistical significance in the logistic regression model. In the multiple stepwise regression, the variables which had significant relationship to depression score were education level, frequencies of going out house, current job and house work activity, regular physical exercise, instrumental ADL, self-rated health and nutritional status, dimness, visual disturbance, and chest pain. In conclusion, main characteristics which had close relationship to the cognitive function and depression symptoms in the studied subjects were physical function and self rated health status.