• Journal of Yeungnam Medical Science
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• v.8 no.1
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• pp.42-52
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• 1991

Two types of urine dipstick assays, Multistix-SG and Combur-9-test RL, were compared for compatibility, accuracy, specificity and predictive values of a positive and negative test in 501 patients urine and artificially prepared specimen. We found that the results of semiquantitative tests of Multistix-SG and Combur-9-Test RL performed were statistically similar in patients specimen. The urinary leukocyte estrase tests of Combur-9-Test RL assays compared with urine sediment microscopy in regard to compatibility, sensitivity, specificity, and predictive values of a positive and negative test 83.7%, 48.1%, 90.3%, 47.4% and 90.1%, respectively. The urinar nitrite tests of Comber-9-Test RL assays compared with urine culture tests, in regard to compatibility, sensitivity, specificity, and predictive values of a positive and negative tests were 90.3%, 19.4%, 84.7%, 53.8% and 94.1%, respectively. For the urinary protein, the sulfosalicylic acid method was the most sensitive test for any kinds of protein, and Multistix-SG appeared more sensitive than Compur-9-Test RL for the albuminuria. For the urinary bilirubin and glusose, two dipstick assays were similar in their diganostic efficiency. Finally in the urinary occult blood tests, Combur-9-Test RL assays was more sensitive than Multistix-SG.

• Journal of dental hygiene science
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• v.9 no.4
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• pp.419-426
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• 2009

The occurrence of toothache signals the malfunction in oral health, which allows the detection of any abnormal condition in the oral cavity at an early stage to prevent the condition from worsening, and thus can act as a preventive measure. This study has looked into the status of oral health management in relation to toothache through the structured survey administered to 235 college students. Based on the survey results, this study aimed at comparing the toothache occurrence prediction between regression analysis and CART model in order to clarify the relationship between the factors of oral health management habits that contribute to toothache occurrence. According to the result, there was a difference between the present health status and the health status of the past year depending on the presence or non-presence of toothache occurrence (p<0.05). There was a difference in the regularity of meal time depending on the presence non-presence of toothache occurrence from the dietary habits of the research subjects (p<0.05). As for the presence or non-presence of toothache occurrence from the oral hygiene habits of the research subject, there was a difference between the occurrence and nonoccurrence of bleeding during brushing or flossing (p<0.05). According to the results of regression analysis, no factors were signifiant in the relationship with the presence or non-presence of toothache occurrence from the status of life habits and oral hygiene habits. 70% of the researched group was randomly selected as the sample for generating an analytical model and the remaining 30% was used as the sample for generating an evaluation model. According to the results of CART model, the occurrence of toothache was higher in the case of irregular meal time and poor current health condition than the case of average or satisfactory health condition. The above results imply that CART model is very useful technique in predicting toothache occurrence compared to regression analysis, and suggests that CART model could be very useful in predicting other oral diseases including toothache.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.126-136
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• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.383-400
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• 2023

Air pollution-related diseases are escalating worldwide, with the World Health Organization (WHO) estimating approximately 7 million annual deaths in 2022. The rapid expansion of industrial facilities, increased emissions from various sources, and uncontrolled release of odorous substances have brought air pollution to the forefront of societal concerns. In South Korea, odor is categorized as an independent environmental pollutant, alongside air and water pollution, directly impacting the health of local residents by causing discomfort and aversion. However, the current odor management system in Korea remains inadequate, necessitating improvements. This study aims to enhance the odor management system by analyzing 1,010,749 data points collected from odor sensors located in Osong, Chungcheongbuk-do, using an Ensemble-Based Multi-Region Integrated Odor Concentration Prediction Model. The research results demonstrate that the model based on the XGBoost algorithm exhibited superior performance, with an RMSE of 0.0096, significantly outperforming the single-region model (0.0146) with a 51.9% reduction in mean error size. This underscores the potential for increasing data volume, improving accuracy, and enabling odor prediction in diverse regions using a unified model through the standardization of odor concentration data collected from various regions.

• 한국체육학회지인문사회과학편
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• v.57 no.6
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• pp.263-272
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• 2018

This study examined the association of gait variability and physical fitness with frailty status in older women. In a cross-sectional design, 168 elderly women, aged 65 years and older (75.07±5.40 years), measured body composition, gait parameters gait variability, physical fitness variables, MMSE-DS and CES-D. Subjects were classified as robust, pre-frail, and frailty based on the Fried et al.(2001) criteria for frailty weight loss, exhaustion, low hand-grip strength, low gait speed, and physical inactivity. Logistic regression analyses were used to determine the odds ratio (ORs) and 95% confidence interval (CI) of frailty status for having gait variability and physical fitness levels. Compared to the robust group (OR=1), the frailty group had significantly higher ORs of having terminal double limb stance (OR=1.48, 95% CI=0.10-2.21, p=.049), step cadence (OR=2.06, 95%CI=1.20-3.43, p=.009) variability, and significantly lower ORs of having upper-strength (OR=0.49, 95%CI=0.31-0.77, p=.002) even after adjusting for age, education, comorbidity, K-IADL, MMSE-KC and CES-D score. The finding of this study suggested that terminal double limb stance, step cadence and upper body muscular strength were independent predictors of frailty.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.89-102
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• 2014

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.775-777
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• 2012

양돈산업에서의 인공수정(Artificial Insemination, AI) 기술은 1994년 이후 본격적으로 국내 양돈농가에 보급되어 양돈 산업 발전에 기초가 되었다. 이러한 중요성에도 불구하고 전국 AI센터의 정액 생산 및 공급체계에 관한 통합관리는 전무한 상태이며, 전국 50여개의 AI센터에서 생성된 정보는 독립된 개별시스템에 의해 관리되고 있다. 이는 전염성이 강한 질병 발생에 대한 대응방안 모색과 전국적인 돼지개량 정책 등을 종합적으로 수립할 때 통합정보관리 분석의 한계점을 가지게 한다. 이에 웹을 기반으로 전국 AI센터의 정액생산 및 공급체계 등에 관하여 통합할 수 있는 AI PigMoS 시스템을 제안하고 구현하였다. 본 논문에서 제안한 AI PigMoS 시스템은 웹을 기반으로 전국 AI센터의 정보를 통합관리 운영할 수 있다. 제안된 시스템은 웅돈, 정액생산 및 판매관리 등에 대해서 이력추적을 할 수 있도록 설계하였으며, 고객관리, 회계, 통계 및 경영관리 등에 대해서는 통합적으로 관리 운영할 수 있도록 설계하였다. 이는 전국 AI센터의 효율적인 관리운영 뿐만 아니라 통합된 AI센터 관련정보의 분석 및 미래 예측자료 등으로 활용되어 효율적인 돼지개량 체계를 구축할 것으로 기대한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.683-687
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• 2009

하천의 하상은 장기간에 걸친 교란으로 변화를 겪으며 자연 평형상태에 이르게 된다. 하천에서의 교란은 자연적인 교란과 인위적인 교란으로 나눌 수 있다. 자연적인 교란은 홍수, 태풍, 지진, 해충, 질병, 사태, 극고 저온, 한발 등을 포함하며 생태계의 안정성, 저항성 및 복원성에 의하여 자연 복원되는 경우이며, 인위적인 교란은 댐건설, 하도정비, 골재채취 기타 다른 하천 구조물의 축조 등으로 인해 생태계의 구조와 기능에 막대한 변화가 초래되는 경우이다. 따라서 이러한 교란된 하천의 하상변동 경향을 분석하고 예측하여 이 치수 구조물 축조에 반영하고 하천 생태계 교란의 영향을 최소화 시키는 것이 하천 환경적 측면에서 매우 중요하다고 할 수 있다. 본 연구에서는 경상북도 안동시 임하면 임하리에 위치한 반변천의 임하 역조정지댐 직하류부터 낙동강 합류점까지 10.92km 지역을 연구대상지역으로 선정하여, 미공병단에서 개발된 하상변동 수치모형인 HEC-6를 이용하여 댐 건설전과 건설 후에 대하여 하상변동을 모의하였다. 그 결과 처음 5년간은 하상이 $0.5{\sim}1.0m$정도 저하되었으나 10년이 지났을 때, 댐 건설전의 경우에는 $-0.4{\sim}2.2m$, 댐 건설 후에는 $-0.2{\sim}1.3m$정도로 하상이 변화되었다. 이는 댐이 없을 경우에는 처음 5년간은 하상이 심하게 저하되었다가 10년이 지나면서 하상 저하율이 감소되면서 평형상태를 이루는데 댐이 있을 경우에는 하상이 저하되는 감소율이 더 적어지면서 댐이 있을 때보다 하상저하가 적게 이루어진 것 때문으로 사료된다.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.1
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• pp.81-88
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• 2014

Accurate wind data is essential for predicting airborne spread of virus. OpenFOAM was used for computational fluid dynamics (CFD) simulation procedure which is under GNU GPL (General Public License). Using complex terrain, DEM (Digital Elevation Map) that was prepared from GIS information covering a research site is converted to a three dimensional surface mesh that is composed by quad and full hexahedral space meshes. Around this surface mesh, an extended computational domain volume was designed. Atmospheric flow boundary conditions were used at inlet and roughness height and was considered at terrain by using rough wall function. Two different wind conditions that was relatively stable during certain periods were compared in 3 different locations for validating the accuracy of the CFD computed solution. The result shows about 10 % of difference between the calculated result and measured data. This procedure can simulate a prediction of time-series data for airborne virus spread that can be used to make a web-based forecasting system of airborne virus spread.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.329-342
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• 2018

Nomogram is a statistical tool that visualizes the risk factors of the disease and then helps to understand the untrained people. This study used risk factors of chronic obstructive pulmonary disease (COPD) and compared with logistic regression model and naïve Bayesian classifier model. Data were analyzed using the Korean National Health and Nutrition Examination Survey 6th (2013-2015). First, we used 6 risk factors about COPD. We constructed nomogram using logistic regression model and naïve Bayesian classifier model. We also compared the nomograms constructed using the two methods to find out which method is more appropriate. The receiver operating characteristic curve and the calibration plot were used to verify each nomograms.