• Health Policy and Management
• /
• v.23 no.4
• /
• pp.376-386
• /
• 2013

Purpose: The purpose of study is to estimate the number of chronic disease patients and medical care expenditure at the time baby-boomers belong to 65 years old aging population, and compare with current 65 year-old aging population. Methods: Analysis method used an estimating formula devised by the researcher and estimated the number of chronic disease patients and medical care expenditure of each generation. Results: When comparing the estimated number of chronic diseases patients of each generation, 40.6% of the first generation, 76.4% of the second generation, 95.2% of third generation are expected to get chronic disease. When comparing each generation's total medical care expenditure, based on the estimated number of chronic diseases patients of each generation, the second generation( 1,206,251,224 thousand won) showed higher than other generation. This study compared the number of chronic disease patients and medical care expenditure between the second generation of the elderly and current elder generation. As a result, the second generation patients was higher than the fourth generation in high blood pressure, diabetes, psychological and behavioral disorder, and neurological diseases whereas the fourth generation is only high the number of patients in heart disease. As for total medical care expenditure, the second generation paid more in high blood pressure, psychological and behavioral disorder while the fourth generation in neurological disease and heart disease. Conclusion: It is desired that considering the number of chronic disease patients and medical care expenditure of baby boomers accounting for 14.6% of total population, in-depth follow-up study is carried out that inquires into what are issues with a current chronic disease management project, what business is needed in order to manage these issues, and how to fund to cover increasing medical care expenditure.

• Journal of Biomedical Engineering Research
• /
• v.40 no.2
• /
• pp.62-67
• /
• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Journal of Sasang Constitutional Medicine
• /
• v.14 no.2
• /
• pp.18-24
• /
• 2002

This research is purposed to find methods of treatment on allergy diseases, through summarizing thought on human and etiology, classification and treatment on 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. Dong mu thought that human is composed of Heart that inside preserve soul and Body that outside respond to Affairs-Objects. 3. The cause of disease is classified into interior cause and exterior cause. Interior cause could be used in cause of disease, exterior cause could be used in prevention of illness, treatment of disease and preservation of health. 4. The treatment of disease proposed in ${\ulcorner}$Dongyi Soose Bowon Sasang Chobongyun(東醫壽世保元四象草本卷${\lrcorner}$ is that it is to recover 'Essential Qi of Constitution(體質正氣)' by medicine and management of 'Mind-Body(心身)' and that chronic disease is treated chiefly by management but acute disease is treated chiefly by medicine. 5. Allergy disease should be prevented by management of 'Mind-Body(心身)'. but if we suffer from allergy disease, we should treat disease through recovering 'Essential Qi of Constitution(體質正氣)' both medicine and management of 'Mind-Body(心身)'.

• Journal of Chest Surgery
• /
• v.11 no.4
• /
• pp.535-540
• /
• 1978

Between January 1974 and November 1978, 23 cases of double valve replacement were done in the Department of Thoracic Surgery, Seoul National university Hospital. All had symptoms of rheumatic valvular heart disease and belonged to functional class III or IV according to NYHA classification. Among 23 cases, mitral and aortic valves were replaced in 14, and mitral and tricuspid valves in 9 cases. Six operative deaths [26%] and 4 late deaths [23%] were found. In the former group 5 and in latter one operative death were noted. Main cause of operative death was low cardiac output syndrome due to myocardial failure. Among 4 late deaths, 2 were caused by thromboembolism, one by bacterial endocarditis, and one by arrhythmia.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.11a
• /
• pp.742-745
• /
• 2017

본 논문은 심장질환 도메인에 데이터 마이닝 기법을 적용한 연구로, 기존 환자의 정보에 대하여 K-최근접 이웃 알고리즘을 통해 결측 값을 대체하고, 대표적인 예측 분류기인 나이브 베이지안, 소포트 벡터 머신, 그리고 다층 퍼셉트론을 적용하여 각각 결과를 비교 및 분석한다. 본 연구의 실험은 K 최적화 과정을 포함하고 10-겹 교차 검증 방식으로 수행되었으며, 비교 및 분석은 정확도와 카파 통계치를 통해 판별한다.

• Progress in Superconductivity
• /
• v.7 no.1
• /
• pp.41-45
• /
• 2005

The diagnostic management of patients with chest pain remains a clinical challenge. Magnetocardiography (MCG) has been proposed as a new non-invasive method for detection of myocardial ischemia. To date, however, MCG technique is not intensively introduced for clinical use. One of the main reasons might be the absence of statistically valid and diagnostically clean criteria, which can determine the presence of certain heart disease. In this work, we suggested a new method to classify the diagnostic value of MCG for the detection of coronary artery disease (CAD) in patients with chest pain. MCG was recorded for three groups (healthy subjects and patients without and with CAD) by means of the 64 channel SQUID gradiometer system installed at a hospital. Using four parameters, which were found to be significantly different between groups, we evaluated a probability, in which parameters can be classified into each group based on the distribution function of the parameter in each group. For all parameters, sum of probabilities was compared between groups to determine the presence of CAD. Our classification method shows that the MCG can be a useful tool to predict the presence of CAD with sensitivity and specificity of higher than $80\%$ each.

• Journal of Preventive Medicine and Public Health
• /
• v.34 no.4
• /
• pp.309-315
• /
• 2001

Objective : To develop a boner understanding of the relationship between weight status and the prevalence of obesity related diseases in the Korean population. Methods : The 1998 Korean National Health and Nutrition Survey was used and 10,880 persons who had previously taken health examinations were selected for study. The Korean Society for the Study of Obesity's classification of weight status was used. Hypertension, diabetes mellitus, dyslipidemia, osteoarthritis, chronic heart disease, stroke were included as obesity related disease. A logistic regression model was developed to estimate the prevalence odds ratio by obesity class adjusted for demographic and socioeconomic factors and we converted the odds ratio to a prevalence ratio using the base line prevalence of disease to aid in the interpretation of the ratios. Results : The prevalence of obesity was 26.3% based on the KSSO classification $(BMI\geq25)$. A graded increase in the prevalence ratio was observed with increasing severity of overweight and obesity for all health outcomes with the exception of chronic heart disease in men and stroke in both men and women. With normal weight individuals as the reference, for men who were younger than 50 years, the prevalence ratios were highest for hypertension BMI<23-25: 1.70(95% CI=1.41-2.05), 25$BMI\geq30$: 4.83(95% CI=3.70-5.84). The prevalence ratios for dyslipidemia were as high as hypertension, but were lower than hypertension for diabetes mellitus and osteoarthritis. Prevalence ratios generally were greater in younger adults. The prevalence of having 2 or more obesity related diseases increased with weight status category, except in people who were older than 50 years. Conclusions : Based on results, obesity is an increasingly important health problem in Korea and the disease burden increases according to weight status. For Korean adults, the strongest relationship was seen between weight status and hypertension and dyslipidemia. In older people the impact of excess weight and obesity is stronger than that seen in younger people. Increased efforts in the study of obesity and prevention and treatment of obesity and obesity related disease are required.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.279-284
• /
• 2020

In January 2020, COVID19 plunged the whole planet into a pandemic. This has caused great economic losses and is causing social confusion. COVID19 has a superior infection rate among people with underlying disease such as heart disease, high blood pressure, diabetes, stroke, depression, and cancer. In addition, it was studied that patients with underlying disease had a higher fatality rate than those without underlying disease. In this study, the presence or absence of underlying disease was classified using EEG data. The data used to classify the presence or absence of underlying disease was EEG data provided by Data Science lab, consisting of 33 features and 69 samples. Z-score was used for data pretreatment. Classification was performed using the neural network NEWFM and ZNN engine. As a result of the classification of the presence or absence of the underlying disease, the experimental results were 77.945 for NEWFM and 76.4% for ZNN. Through this study, it is expected that EEG data can be measured, the presence or absence of an underlying disease is classified, and those with a high infection rate can be prevented from COVID19. Based on this, there is a need for research that can subdivide underlying disease in the future and research on the effects of each underlying disease on infectious disease.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.127-134
• /
• 2022

Generally developed for medical testing, electrocardiogram (ECG) recordings seizure the cardiac electrical signals from the surface of the body. ECG study can consequently be a vital first step to support analyze, comprehend, and expect cardiac ailments accountable for 31% of deaths globally. Different tools are used to analyze ECG signals based on computational methods, and explicitly machine learning method. In all abovementioned computational simulations are prevailing tools for cataloging and clustering. This review demonstrates the different effective methods for heart disease based on computational methods for ECG analysis. The accuracy in machine learning and three-dimensional computer simulations, among medical inferences and contributions to medical developments. In the first part the classification and the methods developed to get data and cataloging between standard and abnormal cardiac activity. The second part emphases on patient analysis from entire ECG recordings due to different kind of diseases present. The last part represents the application of wearable devices and interpretation of computer simulated results. Conclusively, the discussion part plans the challenges of ECG investigation and offers a serious valuation of the approaches offered. Different approaches described in this review are a sturdy asset for medicinal encounters and their transformation to the medical world can lead to auspicious developments.

• ETRI Journal
• /
• v.22 no.2
• /
• pp.1-10
• /
• 2000

Pattern recognition is one of the most common problems encountered in engineering and scientific disciplines, which involves developing prediction or classification models from historic data or training samples. This paper introduces a new approach, called the Representational Capability (RC) algorithm, to handle pattern recognition problems using radial basis function (RBF) models. The RC algorithm has been developed based on the mathematical properties of the interpolation and design matrices of RBF models. The model development process based on this algorithm not only yields the best model in the sense of balancing its parsimony and generalization ability, but also provides insights into the design process by employing a design parameter (${\delta}$). We discuss the RC algorithm and its use at length via an illustrative example. In addition, RBF classification models are developed for heart disease diagnosis.