• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.1
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• pp.138-143
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• 2011

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. As a part of researches for standardization and objectification of differentiation of syndromes for stroke, in this present study, we tried to develop the statistical diagnostic tool discriminating the 4 subtypes of syndrome differentiation using the essential indices considering the sex. Discriminant analysis was carried out using clinical data collected from 1,448 stroke patients who was identically diagnosed for the syndrome differentiation subtypes diagnosed by two clinical experts with more than 3 year experiences. Empirical discriminant model(V) for different sex was constructed using 61 significant symptoms and sign indices selected by stepwise selection. We comparison. We make comparison a between discriminant model(V) and discriminant model(IV) using 33 significant symptoms and sign indices selected by stepwise selection. Development of statistical diagnostic tool discriminating 4 subtypes by sex : The discriminant model with the 24 significant indices in women and the 19 significant indices in men was developed for discriminating the 4 subtypes of syndrome differentiation including phlegm-dampness, qi-deficiency, yin-deficiency and fire-heat. Diagnostic accuracy and prediction rate of syndrome differentiation by sex : The overall diagnostic accuracy and prediction rate of 4 syndrome differentiation subtypes using 24 symptom and sign indices was 74.63%(403/540) and 68.46%(89/130) in women, 19 symptom and sign indices was 72.05%(446/619) and 70.44%(112/159) in men. These results are almost same as those of that the overall diagnostic accuracy(73.68%) and prediction rate(70.59%) are analyzed by the discriminant model(IV) using 33 symptom and sign indices selected by stepwise selection. Considering sex, the statistical discriminant model(V) with significant 24 symptom and sign indices in women and 19 symptom and sign indices in men, instead of 33 indices would be used in the field of oriental medicine contributing to the objectification of syndrome differentiation with parsimony rule.

• Korean Journal of Radiology
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• v.21 no.9
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• pp.1055-1064
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• 2020

Objective: To assess the incremental prognostic value of coronary computed tomography angiography (CCTA) in comparison to a clinical risk model (Framingham risk score, FRS) and coronary artery calcium score (CACS) for future cardiac events in ischemic stroke patients without chest pain. Materials and Methods: This retrospective study included 1418 patients with acute stroke who had no previous cardiac disease and underwent CCTA, including CACS. Stenosis degree and plaque types (high-risk, non-calcified, mixed, or calcified plaques) were assessed as CCTA variables. High-risk plaque was defined when at least two of the following characteristics were observed: low-density plaque, positive remodeling, spotty calcification, or napkin-ring sign. We compared the incremental prognostic value of CCTA for major adverse cardiovascular events (MACE) over CACS and FRS. Results: The prevalence of any plaque and obstructive coronary artery disease (CAD) (stenosis ≥ 50%) were 70.7% and 30.2%, respectively. During the median follow-up period of 48 months, 108 patients (7.6%) experienced MACE. Increasing FRS, CACS, and stenosis degree were positively associated with MACE (all p < 0.05). Patients with high-risk plaque type showed the highest incidence of MACE, followed by non-calcified, mixed, and calcified plaque, respectively (log-rank p < 0.001). Among the prediction models for MACE, adding stenosis degree to FRS showed better discrimination and risk reclassification compared to FRS or the FRS + CACS model (all p < 0.05). Furthermore, incorporating plaque type in the prediction model significantly improved reclassification (integrated discrimination improvement, 0.08; p = 0.023) and showed the highest discrimination index (C-statistics, 0.85). However, the addition of CACS on CCTA with FRS did not add to the prediction ability for MACE (p > 0.05). Conclusion: Assessment of stenosis degree and plaque type using CCTA provided additional prognostic value over CACS and FRS to risk stratify stroke patients without prior history of CAD better.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.6
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• pp.1460-1464
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• 2009

In spite of many studies on statistical model for pattern identifications (PIs), little attention has been paid to the complexity of pattern diagnosis processed by oriental physicians. The aim of this study is to develop a statistical diagnostic model which discriminates four PIs using multiple indicators in stroke. Clinical data were collected from 981 stroke patients and 516 data of which PIs were agreed by two independent physicians were included. Discriminant analysis was carried out using clinical indicators such as symptoms and signs which referred to pattern diagnosis, and applied to validation samples which contained all symptoms and signs manifested. Four Fischer's linear discriminant models were derived and their accuracy and prediction rates were 93.2% and 80.43%, respectively. It is important to consider the pattern diagnosis processed by oriental physicians in developing statistical model for PIs. The discriminant model developed in this study using multiple indicators is valid, and can be used in the clinical fields.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.317-325
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• 2021

Pine wilt disease first appeared in Busan in 1998; it is a serious disease that causes enormous damage to pine trees. The Korean government enacted a special law on the control of pine wilt disease in 2005, which controls and prohibits the movement of pine trees in affected areas. However, existing forecasting and control methods have physical and economic challenges in reducing pine wilt disease that occurs simultaneously and radically in mountainous terrain. In this study, the authors present the use of a deep learning object recognition and prediction method based on visual materials using an unmanned aerial vehicle (UAV) to effectively detect trees suspected of being infected with pine wilt disease. In order to observe pine wilt disease, an orthomosaic was produced using image data acquired through aerial shots. As a result, 198 damaged trees were identified, while 84 damaged trees were identified in field surveys that excluded areas with inaccessible steep slopes and cliffs. Analysis using image segmentation (SegNet) and image detection (YOLOv2) obtained a performance value of 0.57 and 0.77, respectively.

• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.313-320
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• 2022

The KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD) was launched in 2011 with the support of the Korea Disease Control and Prevention Agency. The study was designed with the aim of exploring the various clinical features and characteristics of chronic kidney disease (CKD) in Koreans, and elucidating the risk factors for CKD progression and adverse outcomes of CKD. For the cohort study, nephrologists at 9 tertiary university-affiliated hospitals participated in patient recruitment and follow-up. Biostatisticians and epidemiologists also participated in the basic design and structuring of the study. From 2011 until 2016, the KNOW-CKD Phase I recruited 2238 adult patients with CKD from stages G1 to G5, who were not receiving renal replacement therapy. The KNOW-CKD Phase II recruitment was started in 2019, with an enrollment target of 1500 subjects, focused on diabetic nephropathy and hypertensive kidney diseases in patients with reduced kidney function who are presumed to be at a higher risk of adverse outcomes. As of 2021, the KNOW-CKD investigators have published articles in the fields of socioeconomics, quality of life, nutrition, physical activity, renal progression, cardiovascular disease and outcomes, anemia, mineral bone disease, serum and urine biomarkers, and international and inter-ethnic comparisons. The KNOW-CKD researchers will elaborate a prediction model for various outcomes of CKD such as the development of end-stage kidney disease, major adverse cardiovascular events, and death.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.701-710
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• 2012

Retinitis Pigmentosa(RP) is a common hereditary disease. While they have been normally living, those who have this symptom feel frustration and pain by the damage of visual acuity. At the national level, the loss of the economic activity due to the reduction of economically active population will be also greater. There is an urgent need for the base study that can provide the clinical prognosis information of RP disease. In this study, we suggest that it is possible to predict prognosis through the pattern classification of RP data. Statistical processing results through statistical software like SPSS(Statistical Package for the Social Service) were mainly applied for the conventional study in data analysis. However, machine learning and automatic pattern classification was applied to this study. SVM(Support Vector Machine) and other various pattern classifiers were used for it. The proposed method confirmed the possibility of prognostic prediction based on the result of automatically classified RP data by SVM classifier.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.289-294
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• 2020

Peptic ulcer disease is a gastrointestinal disorder caused by Helicobacter pylori infection and the use of nonsteroid anti-inflammatory drugs. While many studies have been conducted to find the risk factors of peptic ulcers, there are no studies on the suggestion of peptic ulcer prediction models for Koreans. Therefore, the purpose of this study is to implement peptic ulcer prediction model using machine learning based on demographic information, obesity information, blood information, and nutritional information for middle-aged and elderly people. For model building, wrapper-based variable selection method and naive Bayes algorithm were used. The classification accuracy of the female prediction model was the area under the receiver operating characteristics curve (AUC) of 0.712, and males showed an AUC of 0.674, which is lower than that of females. These results can be used for prediction and prevention of peptic ulcers in the middle and elderly people.

• Journal of Preventive Medicine and Public Health
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• v.53 no.4
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• pp.256-265
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• 2020

Objectives: We compared the associations of 3 computed tomography (CT)-based abdominal adiposity indexes with non-alcoholic fatty liver disease (NAFLD) among middle-aged Korean men and women. Methods: The participants were 1366 men and 2480 women community-dwellers aged 30-64 years. Three abdominal adiposity indexes-visceral fat area (VFA), subcutaneous fat area (SFA), and visceral-to-subcutaneous fat ratio (VSR)-were calculated from abdominal CT scans. NAFLD was determined by calculating the Liver Fat Score from comorbidities and blood tests. An NAFLD prediction model that included waist circumference (WC) as a measure of abdominal adiposity was designated as the base model, to which VFA, SFA, and VSR were added in turn. The area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were calculated to quantify the additional predictive value of VFA, SFA, and VSR relative to WC. Results: VFA and VSR were positively associated with NAFLD in both genders. SFA was not significantly associated with NAFLD in men, but it was negatively associated in women. When VFA, SFA, and VSR were added to the WC-based NAFLD prediction model, the AUC improved by 0.013 (p<0.001), 0.001 (p=0.434), and 0.009 (p=0.007) in men and by 0.044 (p<0.001), 0.017 (p<0.001), and 0.046 (p<0.001) in women, respectively. The IDI and NRI were increased the most by VFA in men and VSR in women. Conclusions: Using CT-based abdominal adiposity indexes in addition to WC may improve the detection of NAFLD. The best predictive indicators were VFA in men and VSR in women.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.107-114
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• 2017

Iris diagnosis is an alternative medicine to diagnose the disease of the patient by using different of the iris pattern, color and other characteristics. This paper proposed a disease prediction algorithm that using the iris regions that analyze iris change to using differential image of iris image. this method utilize as patient's health examination according to iris change. Because most of previous studies only find a sign pattern in a iris image, it's not enough to be used for a iris diagnosis system. We're developed an iris diagnosis system based on a iris images processing approach, It's presents the extraction algorithms of 8 major iris signs and correction manually for improving the accuracy of analysis. As a result, PNSR of applied edge detection image is about 132, and pattern matching area recognition presented practical use possibility by automatic diagnostic that presume situation of human body by iris about 91%.