• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Health Policy and Management
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• v.23 no.1
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• pp.35-43
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• 2013

Background: Diagnosis procedure combination (DPC) has recently been introduced in Korea as a demonstration project and it has aimed the improvement of accuracy in bundled payment instead of Diagnosis related group (DRG). The purpose of this study is to investigate that the model of end-stage liver disease (MELD) score as the severity classification of liver diseases is adequate for improving reimbursement of DPC. Methods: The subjects of this study were 329 patients of liver disease (Korean DRG ver. 3.2 H603) who had discharged from National Health Insurance Corporation Ilsan Hospital which is target hospital of DPC demonstration project, between January 1, 2007 and July 31, 2010. We tested the cost differences by severity classifications which were DRG severity classification and clinical severity classification-MELD score. We used a multiple regression model to find the impacts of severity on total medical cost controlling for demographic factor and characteristics of medical services. The within group homogeneity of cost were measured by calculating the coefficient of variation and extremal quotient. Results: This study investigates the relationship between medical costs and other variables especially severity classifications of liver disease. Length of stay has strong effect on medical costs and other characteristics of patients or episode also effect on medical costs. MELD score for severity classification explained the variation of costs more than DRG severity classification. Conclusion: The accuracy of DRG based payment might be improved by using various clinical data collected by clinical situations but it should have objectivity with considering availability. Adequate compensation for severity should be considered mainly in DRG based payment. Disease specific severity classification would be an alternative like MELD score for liver diseases.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.41-49
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• 2021

The causes of climate change are natural and artificial. Natural causes include changes in temperature and sunspot activities caused by changes in solar radiation due to large-scale volcanic activities, while artificial causes include increased greenhouse gas concentrations and land use changes. Studies have shown that excessive carbon use among artificial causes has accelerated global warming. Climate change is rapidly under way because of this. Due to climate change, the frequency and cycle of infectious disease viruses are greater and faster than before. Currently, the world is suffering greatly from coronavirus infection-19 (COVID-19). Korea is no exception. The first confirmed case occurred on January 20, 2020, and the number of infected people has steadily increased due to several waves since then, and many confirmed cases are occurring in 2021. In this study, we conduct a study on climate change before and after COVID-19 using weather data from Korea to determine whether climate change affects infectious disease viruses through logistic regression analysis. Based on this, we want to classify before and after COVID-19 through a logistic regression model to see how much classification rate we have. In addition, we compare monthly classification rates to see if there are seasonal classification differences.

• Science of Emotion and Sensibility
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• v.10 no.2
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• pp.147-154
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• 2007

Electrocardiogram(ECG) being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many researches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm in the con due to inaccuracy of domestic diagnosis results for a heart disease. This paper proposes ST-segment extraction technique diagnosing heart disease parameter from raw ECG data. As the ST-segment is used for prediction of Coronary Artery Disease, we can predict heart disease using classification approach in data mining technique. We can also predict patient's clinical characterization from patient clinical data.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.50-57
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• 2020

The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.1-14
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• 2007

To overcome the limits of prevention and treatment against Diabetes Mellitus(DM) in Western medicine, there have been tendency finding solutions in traditional medicine based on Sogal. But Sogal had been so various concepts, classification and names. As a result there has been confusion in applying Sogal treatment to DM. So in order to clarify, it is necessary to study Sogal concepts and classification historically. The results of studying changes of Sogal concepts and classification are following : Untill AD 8 century, Sogal had not been only syndrome but also disease with throat and urinating difficulties. From 10c to 13c, Sogal had been divided three types in addition to the theories of internal organs, Samcho and complications. From 13c to 14c, the three types of Sogal theory was improved by various medical theory. But still Sogal covered the concepts of syndrome and disease. After 16c, in Chosun Dynasty. concepts of syndrome was eliminated and concepts of disease was strengthend by accounts on pathology, prognosis. complications and malignities. This tendeny was showed well in ${\ulcorner}DongEuiBoGam{\lrcorner}$ and connected to post doctors in Chosun. It was distiction with Chinese Traditional Medicine's view regarding Sogal as syndrome and disease as well, up to the present.

• Journal of Trauma and Injury
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• v.22 no.1
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• pp.108-115
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• 2009

Purpose: We designed a retrospective study to measure the accuracy of the ICD-10 (International Classification of Disease-10) code for trauma patients. We also analyzed the error of the ICISS (International Classification of Disease based Injury Severity Score) due to a missing or an incorrect ICD-10 code. Methods: For the measuring the accuracy of the ICD-10 code for trauma patients in a tertiary teaching hospital's emergency department, two board certified emergency physician performed a retrospective chart review. The ICD-10 code was classified as a main code or a sub-code. The main code was defined as the code of the main department of treatment, and the sub-code was defined as a code other than the main code. We calculated and compared two ICISS for each patient one by using both the existing code and the other by using a corrected code. We compared the proportions of severe trauma (defined as an ICISS less than 0.9) between when the existing code and the corrected code was used respectively. Results: We reviewed the records of 4287 trauma patients who had been treated from July 2008 to November 2008. The accuracy of the main code, the sub-code of emergency department, main-code, the sub-code of hospitalized patients were 97.1%, 59.8%, 98.2% and 57.0%, respectively. Total accuracy of the main and sub-code of emergency department and of hospitalized patients were 91.4% and 58.6%. The number of severe trauma patients increased from 33 to 49 when the corrected code was used in emergency department and increased from 35 to 60 in hospitalized patients. Conclusion: The accuracy of the sub-code was lower than that of the main code. A missing or incorrect subcode could cause an error in the ICISS and in the number of patients with severe trauma.

• Korean Journal of Oriental Medicine
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• v.5 no.1
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• pp.81-100
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• 1999

The result from the research on the disease classifications of the traditional medicine in China, Japan, Taiwan, and North Korea are followings: 1. It is remarkable that China has two different classifications. One is of the diseases named by western medicine and the other is of the syndromes compounded with parts, characters, and pathology of the diseases. The Traditional Chinese Medicine has 615 codes for diseases in 7 departments, and 1684 codes for syndromes. It seems that they have tried to match each disease named by the traditional chinese medicine to each one named by western medicine. But, they have left the diseases impossible to be equivalent to the ones in western medicine themselves and used the same codes of western medicine when the diseases are the same ones in western medicine. 2. In Taiwan, they try to connect the diseases named by the traditional medicine to the ones named by western medicine based on ICD-9. But, they did not attempt to classify the diseases of the traditional medicine by its own ways. The names of diseases in Taiwan medicine include both diseases and syndromes. It is limited to name syndromes by the traditional medicine. And, Taiwan medicine follows ICD in naming injuries. 3. Japan has not got the disease classification for the causes of death, but only the Japanese disease classification for the causes of death, a translation 'The international disease classification for the causes of death. Therefore, The diseases named by traditional medicines are excluded in the public medicine by some Japanese medicines which diagnose through the western medicine and treat by Wa Kang medicine. 4. I can't find out the data over the disease classification for the causes of death by traditional medicine in North Korea. Instead, I can refer to case histories in which differentiation of symptoms and signs and points about them by traditional medicine and the final diagnoses and report about examination by the western medicine has been recorded. In conclusion, It is a distinctive feature that they connect the diseases and the syndromes by the traditional medicine to the ones by the western medicine, and don't tell the diseases from the syndromes.

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

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.208-217
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• 2020

The classification of plant diseases by images captured by a camera sensor has been studied over past decades. A method that has gained much interest is to use image segmentation, from which statistical features are derived and analyzed by machine learning. Recently, deep learning has been adopted in this area. However, image segmentation is still a difficult task to achieve stable performance due to a variety of environmental variations. The end-to-end learning in neural network has a demerit that train images may be different from real images acquired in outdoor fields. To solve these problems, we propose superpixel-based disease classification method using end-to-end CNN (convolutional neural network) learning. Based on experiments performed on PlantVillage apple images, the classification accuracy is 98.29% and 92.43% for full-image and superpixel. As well, the multivariate F1-score is (0.98, 0.93). Therefore we validate that the method of using superpixel is comparable to that of full-image.