• Proceedings of the Korean Society of Veterinary Clinics Conference
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• 2008.05a
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• pp.114-114
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• 2008

• Proceedings of the Korean Society of Veterinary Clinics Conference
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• 2007.10a
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• pp.571-571
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• 2007

• Proceedings of the Korean Society of Veterinary Clinics Conference
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• 2007.10a
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• pp.572-572
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• 2007

• The Plant Pathology Journal
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• v.36 no.5
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• Korean Journal Plant Pathology
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• v.14 no.2
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• pp.150-156
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• 1998

To develop a model for prediction of turnip mosaic virus(TuMV) disease progress of Chinese cabbage based on weather information and number of TuMV vector aphids trapped in Taegwallyeong alpine area, data were statistically processed together. As the variables influenced on TuMV disease progress, cumulative portion(CPT) above 13$^{\circ}C$ in daily average temperature was the most significant, and solar radiation, duration of sunshine, vector aphids and cumulative temperature above $0^{\circ}C$ were significant. When logistic model and Gompertz model were compared by detemining goodness of fit for TuMV disease progress using CPT as independent variable, regression coefficient was higher in the logistic model than in the Gompertz model. Epidemic parameters, apparent infection rate and initial value of logistic model, were estimated by examining the relationship between disease proportion linearized by logit transformation equation, In(Y/Yf-Y) and CPT. Models able to describe the progression of TuMV disease were formulated in Y=100/(1+128.4 exp(-0.013.CPT.(-1(1/(1+66.7.exp(-0.11.day). Calculated disease progress from the model was in good agreement with investigated actual disease progress showing high significance of the coefficient of determination with 0.710.

• Journal of Korean Academy of Nursing
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• v.32 no.1
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• pp.62-76
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• 2002

This study was designed to construct a structural model for health promoting behavior of patients with chronic disease. The hypothetical model was developed based on the literature review and Pender's health promotion model. Method: Data was collected by questionnaires from 1748 patients with chronic disease in General Hospital from December 1999 to July 2000 in Seoul. The disease of subject were cardiac disease included hypertension peptic ulcer, pulmonary disease included COPD and asthma, DM, and chronic kidney disease. Data analysis was done with SAS 6.12 for descriptive statistics and PC-LISREL 8.13 Program for Covariance structural analysis. Results: 1. The fit of the hypothetical model to the data was moderate, it was modified by excluding 4 path and including free parameters to it. The modified model with path showed a good fitness to the empirical data (χ2=591.83, p<.0001, GFI=0.97, AGFI= 0.94, NNFI=0.95, RMSR=0.01, RMSEA=0.05). 2. The perceived benefits, perceived barriers, self-efficacy, self- esteem, and the plan for action were found to have significant direct effect on health promoting behavior of chronic disease. 3. The health concept, health perception, emotional state, social support were found to have indirect effects on health promoting behavior of chronic disease. Conclusion: The derived model in this study is considered appropriate in explaining and predicting health promoting behavior of patients with chronic disease. Therefore, it can effectively be used as a reference model for further studies and suggested implication in nursing practice.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.823-829
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• 2001

Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.

• International Journal of Stem Cells
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• v.15 no.3
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• pp.334-345
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• 2022

Background and Objectives: Flavonoids form the largest group of plant phenols and have various biological and pharmacological activities. In this study, we investigated the effect of a flavonoid, 3, 4'-dihydroxyflavone (3, 4'-DHF) on osteogenic differentiation of equine adipose-derived stromal cells (eADSCs). Methods and Results: Treatment of 3, 4'-DHF led to increased osteogenic differentiation of eADSCs by increasing phosphorylation of ERK and modulating Reactive Oxygen Species (ROS) generation. Although PD98059, an ERK inhibitor, suppressed osteogenic differentiation, another ERK inhibitor, U0126, apparently increased osteogenic differentiation of the 3, 4'-DHF-treated eADSCs, which may indicate that the effect of U0126 on bone morphogenetic protein signaling is involved in the regulation of 3, 4'-DHF in osteogenic differentiation of eADSCs. We revealed that 3, 4'-DHF could induce osteogenic differentiation of eADSCs by suppressing ROS generation and co-treatment of 3, 4'-DHF, U0126, and/or N-acetyl cysteine (NAC) resulted in the additive enhancement of osteogenic differentiation of eADSCs. Conclusions: Our results showed that co-treatment of 3, 4'-DHF, U0126, and/or NAC cumulatively regulated osteogenesis in eADSCs, suggesting that 3, 4'-DHF, a flavonoid, can provide a novel approach to the treatment of osteoporosis and can provide potential therapeutic applications in therapeutics and regenerative medicine for human and companion animals.