• Journal of Sasang Constitutional Medicine
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• v.14 no.3
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• pp.1-6
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• 2002

1.Objects of Research This research is purposed to find methods of treatment on serious diseases, through summarizing etiology, classification and treatment on serious diseases proposed in Sasang constitutional medicine 2.Methods of Research It was researched as bibliologically with Dong-mu's chief medical writings such as ${\ulcorner}$Dongyi Soose Bowon(東醫壽世保元)${\lrcorner}$, ${\ulcorner}$Dongyi Soose Bowon Sasang Chobongyun(東醫壽世保元四象草本卷)${\lrcorner}$ 3.Results and Conclusions 1. The principle of treatment in the previous medicine is to treat each disease by 'Assisting-Good Qi' and 'Removig-Bad Qi'. but The principle of treatment in Sasang Constitutional medicine is to manage incurable disease by helping 'Essential Qi of each constitution(體質正氣)' 2. Incurable disease is classified into a chronic disease by 'Nature(性氣)' and a acute disease by 'Emotion(情慾)'. Both diseases became serious through 'Noi-Ok(牢獄)' and 'Wi-Gyoung(危傾)'. A chronic disease is much in the middle years of life and become senile disease. A acute disease is much in the young years of life and make patients die young. 3. prognoses of incurable disease are different from degree of Inherent vitality(命脈實數) and term of disease. The case in which Inherent vitality is exhausted is thought that is unable to treat. 4. The prevention of incurable disease Is more important the treatment of one in Sasang Constitutional Medicine. but if incurable disease is caught, Medicine(醫藥) and management(調養) must be used together for treatment of incurable disease. Medicine is more important in the level of 'Noi-Ok(牢獄)' and management is more important in the level of 'Wi-Gyoung(危傾)'. 5. Therefore, incurable disease should be treated by method that 'Essential Qi of each constitution(體質正氣)' is recovered and declination is removed through 'controlling mind(治心) and correcting Qi(正氣), so then the state of 'Golden mean(中庸)'　is reached.

• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.3
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• pp.273-279
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• 2013

The objective of this study is to find out a reason why prescriptions have different effects on each patient who has same symptoms or same abdominal examinations and further more to classify the formulas more efficiently. In applying 's prescriptions, it is known that conventional ways such as treatment in accordance with symptoms or abdominal examinations have many advantages and problems reversely. To make up for the problems, additional references like strength of constitutional resistance and location of disease, degree of income and outgo are designed. And the notion in Oriental Medicine embracing aspects mentioned above corresponds to triple energizer. Triple energizer's exuberance-debilitation is able to draw an inference from some factors like density of skin interstices, elasticity of abdomen, appetite, digestive power. According to Exuberance-Debilitation of Triple Energizer, can be divided into five steps: weak(弱)-moderately weak(中弱)-neither weak nor strong(中)-moderately strong(中强)-strong(强). prescriptions would be dealt with those 5 steps, and it would be highly effective and consequently side effects could be reduced. On the basis of this classification method upon formula group, the prescriptions of can be applied more accurately by setting a direction through strength of constitutional resistance and location of disease and combining with existing references like symptoms, palpation and abdominal examinations.

• Journal of Chest Surgery
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• v.28 no.9
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• pp.822-828
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• 1995

This paper reports 15 native valve endocarditis cases had surgical operation in the past 10 years at the department of Cardiovascular and Thoracic Surgery, Chonbuk National University Hospital. In this study, 10 cases out of 15 were in class I or II by the New York Heart Association functional classification. None of the cases had a history of taking addictive drugs. Five cases were congenital heart disease, three cases were rheumatic heart disease and two cases were degenerative heart disease. Thus 10 cases had the underlying disease. All cases had antibiotics treatment for 3 to 6 weeks before operation. In the culture test, only four cases were positive in the blood culture and one case was positive in the excised valve culture. Organisms on blood and valve culture were Streptococcus epidermis, Streptococcus viridans, Staphylococcus aureus and Staphylococcus epidermidis. In the 10 cases without ventricular septal defect, the aortic valve was involved in four, mitral in four, both in two and involved valves in the 5 cases with ventricular septal defect were tricuspid in three, pulmonic in two. Eight cases had operation because they showed moderate congestive heart failure due to valvular insufficiency and vegetation with or without embolism. Seven cases had operation because they showed persistent or progressive congestive heart failure and/or uncontrolled infection. Five cases with ventricular septal defect underwent the closure of ventricular septal defect, vegetectomy and leaflet excision of the affected valves without valve replacement. In the cases without ventricular septal defect, the affected valves were replaced with St. Jude mechanical prosthesis. Postoperative complications were recurrent endocarditis in two, embolism in one, allergic vasculitis in two, spleen rupture in one and postpericardiotomy syndrome in one. At the first postoperative day, one case died of cerebral embolism. At the 11th postoperative month, one case died of recurrent endocarditis and paravalvular leakage in spite of a couple of aortic valve replacement. In the survived cases[13 cases in this study , all cases but one became class I or II by the New York Heart Association functional classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.621-629
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• 2016

This paper presents a decision method of middle ear disease which is developed in children and adults. In the proposed method, features are extracted from the middle ear disease images and normal images using HoG (histogram of oriented gradient) descriptor and the extracted features are learned by SVM (support vector machine) classifier. To obtain an input vector into SVM, an input image is resized to a predefined size and then the resized image is partitioned into 16 blocks each of which is partitioned into 4 sub-blocks (namely cell). Finally, the feature vector with 576 components is given by using HoG with 9 bins and it is used as SVM learning and classification. Input images are classified by SVM classifier based on the model of learning features. Experimental results show that the proposed method yields the precision of over 90% in decision.

• Advances in Traditional Medicine
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• v.4 no.2
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• pp.112-119
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• 2004

Glutathione S-transferase polymorphisms (GST) were examined in 98 cases with cerebrovascular disease (CVD) to test the hypothesis that GST polymorphisms confer a risk to an individual to develop CVD. Tobacco smoke is a major cause of both cancer and vascular disease. We therefore were stratified the subjects with CVD for smoking status, and then examined whether polymorphisms in this detoxification enzyme gene, GST, influence risk of CVD. Neither GSTM1 nor GSTT1 genotypes in the CVD group was significantly different from the control group (n=230), even in smokers. We attempted the combined analyses for GSTM1 and GSTT1 genotypes in CVD for smoking status. No significant association observed between the combined genotypes and CVD. We also classified the subjects and control group into four types according to Sasang Constitutional Medicine, Korean Traditional Oriental Medicine, and investigated the association among GST genotypes, CVD, and Sasang constitutional classification. Our observations do not confirm the effect of the GSTM1 and GSTT1 genotypes as a risk factor for CVD, even in smokers. Furthermore, we first attempted to evaluate the efficacy of Sasang Constitutional Medicine, and to find an association with CVD.

• Research in Community and Public Health Nursing
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• v.16 no.4
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• pp.381-391
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• 2005

Purpose: The purpose of this research is to develop and classify district visiting nursing standards and to standardize visiting nursing service pathways. Method: This research was conducted as a focus group study and analyzed visiting nursing records. We surveyed 201 recipients at urban health centers, who were selected through convenient sampling, from April 2003 to November 2003. Result: First, visiting nursing service recipients were classified into four groups according to household and financial characteristics, existence of disease, ability of self-care, and existence of home care service needs. Standardized pathways of the selected items were assessment. nursing care plan, disease management and promotion of self-care ability for Level I (mean=12.2 visits). For Level II (7.3 visits) were offered assessment. disease management. health education. and health promotion services. For Level III (5.2 visits) were offered assessment. disease management. health education and health promotion services, and for Level IV (2.7 visits) were offered thorough assessment, education for self-care and health promotion. Conclusion: The visiting nursing service pathways identified in this research need to be developed further as basic materials applicable to quality assurance and agency evaluation. For this, we suggest repeated research and test to apply the derived standardized visiting nursing services pathways in visiting nursing programs.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Smart Media Journal
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• v.13 no.7
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• pp.36-44
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• 2024

Identifying plant species and diseases is crucial for maintaining biodiversity and achieving optimal crop yields, making it a topic of significant practical importance. Recent studies have extended plant disease recognition from traditional closed-set scenarios to open-set environments, where the goal is to reject samples that do not belong to known categories. However, in open-world tasks, it is essential not only to define unknown samples as "unknown" but also to classify them further. This task assumes that images and labels of known categories are available and that samples of unknown categories can be accessed. The model classifies unknown samples by learning the prior knowledge of known categories. To the best of our knowledge, there is no existing research on this topic in plant-related recognition tasks. To address this gap, this paper utilizes knowledge distillation to model the category space relationships between known and unknown categories. Specifically, we identify similarities between different species or diseases. By leveraging a fine-tuned model on known categories, we generate pseudo-labels for unknown categories. Additionally, we enhance the baseline method's performance by using a larger pre-trained model, dino-v2. We evaluate the effectiveness of our method on the large plant specimen dataset Herbarium 19 and the disease dataset Plant Village. Notably, our method outperforms the baseline by 1% to 20% in terms of accuracy for novel category classification. We believe this study will contribute to the community.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Preventive Medicine and Public Health
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• v.10 no.1
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• pp.102-108
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• 1977

A study on the sickness distribution and mode of treatment in rural area was conducted during the period from July '75 to Aug. '75 using 1,225 households, 7,918 population (4,017 male, 3,901female) and 343 cases th at found during the period of survey who had beenlived in Nammyon, Hwasoongun, Chonnam. The summarized results were as follows : 1. Average family number per household was 6.5 and prevalence rate was 43.3 (21.2 for male, 22.1 for female). 2. General sickness distribution by classification of disease according to W.H.O. was highest in disease of the nervous system and sense organs (21.3%), and important others were disease of the digestive system (16.9%) and disease of the respiratory system(14.8%). In male, distribution was in order of downward disease of digestive system, disease of nervous system and sense organs, disease of skin, cellular tissue, bones and organs of movement, and disease of respiratory system. In female, distribution was in order of downward disease of nervou s system and sense organs, disease of respiratory system, disease of digestive system, and disease of skin, cellular tssue, bones and organs of movement. 5. Types of treatment in both sexes were showed that home and folkmedicine (41.1%), pharmacy(24.5%), admission to hospital or clinic (16.9%), out-patient clinic (10.8%) and herbmedicine (6.7%) in downward order. Hospital and clinic utility rate was 27.5% (31.5 for male, 24.0 for female) and it was highest in 0-4 age groups and lowest in 40-49 year age groups. 4. Hospital and clinic utility rate was highest in neoplasms, and the other hands, disease of the nervous system and sense organs and disease of the digestive system were the highest groups in the all types of treatment other than hospital and clinic. 5. On the results of treatment not, exactly replied answer was the highest (41.7%) and only 16.0% said complete recovery. In completely recovered cases, hospital and clinic using group was predominant (58.2%) and in aggravated cases, home and folkmedicine using group was highest.