• Pediatric Infection and Vaccine
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• v.16 no.2
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• pp.142-149
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• 2009

Purpose : The aim of this study was to determine the diagnostic value of serum procalcitonin (PCT) compared with that of C-reactive protein (CRP) and the total white blood cell count (WBC) in predicting bacterial infections in febrile infants<6 months of age. Methods : A prospective study was performed with infants <6 months of age who were admitted to the Department of Pediatrics with a fever of uncertain source between July and September 2008. Spinal taps were performed according to clinical symptoms and physical examination. Serum PCT levels were measured using an enzyme-linked fluorescent assay. Results : Seventy-one infants (mean age, 2.62 months) were studied. Twenty-six infants (36.6%) had urinary tract infections (UTIs), and 22 infants (31.0%) had viral meningitis. The remaining infants had acute pharyngitis (n=1), herpangina (n=1), upper respiratory tract infections (n=7), acute bronchiolitis (n=8), acute gastroenteritis (n=4), and bacteremia (n=2). The median WBC and CRP levels were significantly higher in infants with UTIs than in infants with viral meningitis. However, there were no differences in the median PCT levels between the groups (0.14 ng/mL vs. 0.11 ng/mL, P=0.419). The area under the receiver operating characteristic curve was 0.792 (95% CI, 0.65-0.896) for WBC, 0.77 (95% CI, 0.626-0.879) for CRP, and 0.568 (95% CI, 0.417-0.710) for PCT. An elevated WBC count (>11,920/${\mu}L$) and an increased CRP level (>1.06mg/dL) were significant predictors of UTIs based on multiple logistic regression analysis. Conclusion : Serum PCT concentrations should be interpreted with caution in infants <6 months of age with a fever of uncertain source.

• Tuberculosis and Respiratory Diseases
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• v.50 no.2
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• pp.236-244
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• 2001

Background : Most current research using prognostic scoring systems in critically ill patients have focused on prediction using the first intensive care unit (ICU) day data or daily updated data. Usually the mean ICU length of stay in Korea is longer than in the western world. Consequently, a more cost-effective and practical prognostic parameter is required. The principal aim of this study was to assess the prognostic value of the seventh day(7th day : the average mean ICU length of stay) APACHE III score in a medical intensive care unit. Methods : 241 medical ICU patients from July 1997 to April 1998 were enrolled. The 1st and 7th scores were measured by using the APACHE III scoring system and compared between survivors and non-survivors. Logistic regression analysis was performed to determine the relationship between the $1^{st}$ and $7^{th}$ APACHE III scores and the mortality risk. Results : 1 )The mean length of stay in the ICU was $10.3{\pm}13.8$ days. 2)The mean $1^{st}$ and $7^{th}$ day APACHE III scores were $59.7{\pm}30.9$ and $37.9{\pm}27.7$. 3) The mean $1^{st}$ day APACHE III score was significantly lower in survivors than in non- survivors($49.9{\pm}23.8$ vs $86.3{\pm}32.3$, P<0.0001). 4)The mean $7^{th}$ day APACHE III score was significantly lower in survivors than in non- survivors($30.1{\pm}18.5$ vs $80.1{\pm}30.4$, P<0.0001). 5)The odds ratios among the $1^{st}$ and $7^{th}$ day APACHE III scores and the mortality rate were 1.0507 and 1.0779 respectively. Conclusion : These results suggest that the seventh day APACHE III score is as useful in predicting the outcome as is such like the first day APACHE III score. Therefore, in comparison to the daily APACHE III score, measuring the $1^{st}$ and $7^{th}$ day APACHE III scores are also useful for predicting the prognosis of critically ill patients in terms of cost-effectiveness. It is suggested that the $7^{th}$ day APACHE III score is useful for predicting the clinical outcome.

• Clinical and Experimental Pediatrics
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• v.45 no.5
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• pp.575-587
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• 2002

Objective : To investigate the understanding of breast-feeding in pregnant woman and the proper way of encouraging breast-feeding. Methods : Each questionnaire included items about demographic characteristics and the understanding of breast-feeding. The questionnaires were filled up by pregnant women visiting obstetric clinics in Seoul and its vicinities, Busan, Choongjoo and Chungjoo from July 2001 to August 2001. One thousand, two hundred ninety questionnaires were analysed by Chi square tests and multiple logistic regressions. Results : The majority of pregnant women(87.4%) planned breast-feeding. Forty three percent of them had plans to breast-feed for 4-6 months. There were no differences in the level of education, the family size and the source of information about breast-feeding in planning to breast-feed (P>0.05). The main reasons for not choosing to breast-feed were returns to work(41.3%), previous failures of breast-feeding(17.4%), concerns about insufficient amount of breast milk(10.9%), breast and nipple problems(10.3%) and maternal illness(9.4%). The average score on the test of the understanding about breast-feeding was 59.7/100. The average scores on the understanding about the methods and advantages of breast-feeding were 45.3/100 and 86.1/100, respectively. The maternal status of employment, previous history of breast-feeding, the time of decision to breastfeed, person advocating breast-feeding and the understanding on the advantages of breast-feeding were significant determinant factors in planning to breast-feed(P<0.05). Conclusion : Pediatricians should take steps to make an effort to increase the breast-feeding rate and to encourage breast-feeding by timely education. Beyond the medical field, political and social supports for breast-feeding are urgently needed.

• Journal of agricultural medicine and community health
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• v.42 no.3
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• pp.119-131
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• 2017

Objectives: The purpose of this study was to compare the diabetic management indicators between agriculture, forestry, and fishery workers (AFF) and other occupational adults (non-AFF) in community-dwelling diabetes. Methods: The study population consisted of 22,127 diabetic population ${\geq}19years$ who participated in the 2015 Community Health Survey. Chi-square test and logistic regression analysis was used to compare the diabetic management indicators between AFF and non-AFF. Socioeconomic characteristics such as age, gender, education level, monthly household income, National Basic Livelihood Security status, and marital status was sequentially adjusted. Results: Among total diabetic population, 3,712 people (16.8%) was AFF and 18,415 people (83.2%) was non-AFF. The fully-adjusted odds ratio [OR] (95% confidence interval [CI]) of current non-medical treatment (0.72, 0.66-0.79), measurement of hemoglobin A1c (0.61, 0.55-0.67), screening for diabetic retinopathy (0.76, 0.70-0.83), screening for diabetic nephropathy (0.75, 0.70-0.81), non-alcoholic or moderate drinking (0.70, 0.64-0.78), nutrition label reading (0.83, 0.71-0.98), low salt preference (0.85, 0.78-0.93), dental examination (0.60, 0.54-0.66), scaling experience (0.84, 0.77-0.93), regular toothbrushing (0.66, 0.58-0.76), and diabetes management education (0.84, 0.77-0.92) was significantly lower in AFF compared to non-AFF. In contrast, the fully-adjusted OR (95% CI) of AFF's low stress level (1.39, 1.26-1.52) and adequate sleep duration (1.22, 1.13-1.32) was significantly higher than non-AFF, which are better indicators of diabetic management in AFF. Conclusions: Overall, the level of diabetes management of AFF was not as good as that of non-AFF. In order to improve the level of diabetes management of AFF, a delicate diabetes intervention strategy considering the occupational characteristics of AFF will be needed.

• Journal of agricultural medicine and community health
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• v.30 no.1
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• pp.101-111
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• 2005

Objectives: This study was to evaluate the compliance of hypertensive patients and its related factors registered at Community Health Practitioner Post(CHCP). Methods: 304 patients were interviewed by trained nursing students during one month(June~July 2004). The questionnaire included general charactristics, knowledge of hypertension, health education experience, constructs of Health Belief Model, self efficacy and so on. Compliance group was defined "having regularly medication and good life style". Good life style included regular exercise, non-smoking, little alcohol, low salt diet, weight control. Results: In compliance group 90.3% of man and 93.3% of woman were regularly taking hypertensive medicine, and 45.2% of man and 56.4% of woman were having good life style (compliance group). In both man and woman, the group of higher education were more compliance group, but were statistically significant were in man(p<0.05). In woman, the compliance group have significantly higher score in knowledge of hypertension(p(0.05). The compliance group have significantly higher self-efficacy score in both man and woman (p<0.05). In Health Belief Model, susceptibility and benefit were statistically significant in man, seriousness, benefit and barrier in woman(p<0.05). In multiple logistic regression analysis, education level and self efficacy in man and knowledge of hypertension, self-efficacy and benefit in woman were significant variables (p<0.05). Conclusions: It is very important to evaluate and modify life-style adding to having regularly medication in hypertensive patients registered at CHCP. To this, health education programs about benefit to compliance and the methods to improve self-efficacy should be developed for this patients.

• Journal of agricultural medicine and community health
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• v.30 no.1
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• pp.75-88
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• 2005

Objectives: This survey was intended to provide basic data which can be available as a baseline in the set up of dietary guidelines for assuring community-based self-support of the rural elderly, through investigation of the relationship of the various dietary consumptions with their ADL and IADL. Methods: The study subjects, 439 rural residents(male: 196, female:243) aged over 65 in Kumsan Kun and Chongyang Kun, Chungchongnamdo Province were interviewed, in June of 2004, about their sociodemographic characteristics, daily life styles, the variety of dietary consumption, ADL and IADL with the following major findings: Results: In terms of the scores' distribution to show variety of food consumption among all subjects, 68.3% got 1~3 points, 23.2% 4~6 points, and 8.4% 7~10 points with a decreasing proportion of subjects in higher points. In terms of their functional status, normal-range groups showed 93.2% of ADL and 72.9% of IADL whereas, impaired ADL group 6.8% of ADL and 27.1% of IADL, respectively. Concerning the relation of ADL and IADL with the variety of their consumed food, the greater scores for food variety was associated with the significantly higher proportion of normal ADL group and the lower proportion of impaired ADL group. Multiple logistic regression analysis with ADL and IADL as dependent variables, and food variety scores as explanatory variables, the relative risk of impaired-ADL group was 0.84 in the food variety group of 4~6 points, 0.63 in 7~10 points with no statistical significance. The relative risk of impaired- IADL group was 0.52(p<0.01) in the food variety group of 4~6 points, 0.41(p<0.05) in 7~10 points with statistical significance. Conclusions: These study results suggest that the lower dietary variety, the lower functional capacity of daily living, and the variety of dietary is associated with the functional capacity of daily living in rural elderly.

• Journal of agricultural medicine and community health
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• v.25 no.1
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• pp.133-147
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• 2000

To provide data for the improvement of primary health care through the study on the utilization rate of community health practitioner(CHP) post and its related factors toward some rural residents in Mooan County Chollanamdo, a questionnaire survey was made from 382 persons during August 1999. Comparison was made between persons at the seaside area under difficult transportation and persons at the railroad area under convenient transportation. The results are as follows: 1. For the last one year, 83.3% of seaside area residents and 67.0% of railroad area residents used CHP post. As the purpose of visit to CHP post at seaside area, 94.3% visited for medical care, 25.3% for chronic disease control and 22.2% for health counselling and 14.1% for chronic disease control. 2. By simple analysis, sex, age, marital status, educational level, residence area, distance from living village to CHP post, presence of chronic diseases, satisfaction with CHP and confidence on CHP were related significantly with the utilization of CHP post for the last one year. 3. By multiple logistic regression, statistically significant variables related with the utilization rate of CHP post for the last one year were analyzed as age, sex, residence area and distance from living village to CHP post.

• Journal of agricultural medicine and community health
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• v.38 no.2
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• pp.116-129
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• 2013

Objectives: This study was performed to identify the associations of social capital with suicidal thoughts and attempts in Korean communities with poor health. Methods: We used the data from community health interviews conducted at 40 administrative sections (dong, eup, or myeon) with high mortality from August to October in 2010, 2011, and 2012 as part of the Health Plus Happiness Plus Projects in Gyeongsangnam-do Province. The 8,800 study subjects composed of 220 adults systematically sampled from each administrative section were asked if they had thought about suicide or had attempted suicide within 1 year. The social participation was measured with 'participation in formal and/or informal group' and trust using responses to three questions about trust of others. Results: The prevalence of suicidal ideation and attempt within 1 year were 10.4% and 0.8%, respectively. The logistic regression analysis revealed that those who participated in only informal groups, or had highest trust level reported less suicidal ideation, or attempt after adjusting for socio-demographic factors (sex, age, marital status, occupation, and food affordability), self-rated health, and health behaviors (smoking, alcohol drinking, and exercise). Conclusions: This study suggested social capital such as social participation and trust was associated with less suicide ideation and attempt. More studies are warranted for the association of social capital with suicidal behavior.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.