• 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).

• 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.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.103-108
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• 2019

The purpose of this study is to identify the factors that may affect the 5-year survival of colon cancer through network model and to use it as a clinical decision supporting system for colorectal cancer patients. This study was conducted using data from 2,540 patients who underwent colorectal cancer surgery from 1996 to 2018. Eleven factors related to survival of colorectal cancer were selected by consulting medical experts and previous studies. Analysis was proceeded from the data sorted out into 1,839 patients excluding missing values and outliers. Logistic regression analysis showed that age, BMI, and heart disease were statistically significant in order to identify factors affecting 5-year survival of colorectal cancer. Additionally, a correlation analysis was carried out age, BMI, heart disease, diabetes, and other diseases were correlated with 5-year survival of colorectal cancer. Sex was related with BMI, lung disease, and liver disease. Age was associated with heart disease, heart disease, hypertension, diabetes, and other diseases, and BMI with hypertension, diabetes, and other diseases. Heart disease was associated with hypertension, diabetes, hypertension, diabetes, and other diseases. In addition, diabetes and kidney disease were associated. In the correlation analysis, the network model was constructed with the Network Correlation Coefficient less than p <0.001 as the weight. The network model showed that factors directly affecting survival were age, BMI levels, heart disease, and indirectly influencing factors were diabetes, high blood pressure, liver disease and other diseases. If the network model is used as an assistant indicator for the treatment of colorectal cancer, it could contribute to increasing the survival rate of patients.

• Journal of the Korean Mathematical Society
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• v.54 no.3
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• pp.987-997
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• 2017

A deterministic model for the spread of pine wilt disease with asymptomatic carrier trees in the host pine population is designed and rigorously analyzed. We have taken four different classes for the trees, namely susceptible, exposed, asymptomatic carrier and infected, and two different classes for the vector population, namely susceptible and infected. A complete global analysis of the model is given, which reveals that the global dynamics of the disease is completely determined by the associated basic reproduction number, denoted by $\mathcal{R}_0$. If $\mathcal{R}_0$ is less than one, the disease-free equilibrium is globally asymptotically stable, and in such a case, the endemic equilibrium does not exist. If $\mathcal{R}_0$ is greater than one, the disease persists and the unique endemic equilibrium is globally asymptotically stable.