• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권9호
• /
• pp.2512-2545
• /
• 2024

One of the biggest challenges for the medical professionals is spotting cardiovascular issues in the earliest stages. Around the world, Cardiovascular Diseases (CVD) are a major cause of death for almost 18 million people each year. Heart disease is therefore a serious concern that needs to be treated. The numerous elements that affect health, such as excessive blood pressure, elevated cholesterol, aberrant pulse rate, and many other factors, might make it challenging to detect heart disease. Consequently, early disease detection and the development of effective treatments can benefit greatly from the field of artificial intelligence. The purpose of this work is to develop a new IoT based healthcare monitoring framework for the prediction of CVD using machine learning algorithm. Here, the data preprocessing has been performed to create the normalized dataset for improving classification. Then, an Artificial Gorilla Troop Optimization (AGTO) algorithm is deployed to choose the most pertinent features from the normalized dataset. Moreover, the Multi-Linear Regression Classification (MLRC) model is also implemented for accurately categorizing the medical information as whether healthy or CVD affected. The results of the proposed AGTO-MLRC mechanism is validated and compared using the popular benchmarking datasets.

• 대한예방한의학회지
• /
• 제17권2호
• /
• pp.47-68
• /
• 2013

Objectives : This research aimed to develop a new etiologic classification for traditional Korean Medicine in order to respond to the social and environmental change. Methods : We reviewed the existing theories on etiological classification for East Asian Medicine thoroughly and discussed the problems and limitations. Based on the experts' consensus, we abstracted disease factors and etiologic items. Results : The disease factors are classified into three parts: the human body, the environment, and the interaction between the human body and the environment. We defined them as the inner factor, the external factor, and the interaction between the inner and the external factors. The inner factor is free from the influence of the environment, and it causes diseases solely from the components of the human body. It is divided into genetic factors. The external factor is defined as a case when a disease occurs due to a factor outside the human body and includes external injuries, environmental pollution, and natural disasters. The interaction between the inner and the external factors is a disease factor that causes diseases by the interaction of the human body and the environment and includes emotions, habits, and social environment. As a result of the analysis, it was possible to see the meanings at a single glance as the scattered and fractional meanings were integrated with focus on medicinal herbs, but the increasing number of analyzed medicinal herbs tended to more and more complicate their relationships, thus, requiring additional work like filtering. Conclusions : The new etiologic classification of Korean Medicine fully reflects the perspectives on life in Korean Medicine while embracing the changes in modem society. Also, by avoiding the usage of ambivalent terms and wrong classification methods, the new classification system constructs intuitive and concise etiology and improves usability in clinical medicine.

• 응용통계연구
• /
• 제19권1호
• /
• pp.183-193
• /
• 2006

$L_1$-데이터뎁스를 이용한 분류방법(L1DDclass)과 관측치들 사이의 $L_1$-거리를 이용한 분류방법(L1DISTclass)의 특징을 살펴보고, 이 두 방법을 결합한 새로운 분류방법 (DnDclass: Distance and Data-depth based classification)의 효용성을 소개하고자 한다. 모의실험을 통해 세가지 분류방법의 결과를 비교하고 제안된 분류방법이 다양한 경우에 더 효과적일 수 있다는 사실을 확인한다.

• 사상체질의학회지
• /
• 제14권3호
• /
• pp.74-84
• /
• 2002

The purpose of study is 584 patients who had been treated in the Oriental Medical Hospital at Dong Eui Medical Center during 1 year from February 2001 to January 2002. We proceeded the judgment of Sasang constitution by 'Questiinaire of Sasang Constitution Classification(I)' and 'Questiinaire of Sasang Constitution Classification II(QSCCII)'. The following conclusion were made in comparison with Sasang Constitution and Questiinaire about disease and style of symptoms. 1. The subject of 'increase of the weight of a body(gain weight), cough, nasal discharge(rhinorrhea), stuffed nose, sweatier, more like fat food, more like salty food', has significant differences in sasang constitution classification. The frequency of Taeum group is more than of Soeum group and Soyang group. 2. The subject of 'the complextion is bad, anorexia, indigestion, nervousness, sonitus', has significant differences in sasang constitution classification. The frequency of Soeum group is more than of Taeum group and Soyang group. 3. The subject of 'urodynia, otic discharge', has significant differences in sasang constitution classification. The frequency of Soyang group is more than of Taeum group and Soeum group.

• 동의생리병리학회지
• /
• 제23권3호
• /
• pp.540-547
• /
• 2009

There needs disease-predictable signs in order to enable preventive diagnosis and therapy. Then traditional Chinese medicine applies various medical diagnostic equipments used in western medicine to diagnosing sub-healthy state. But such data are not originated from inherent oriental medicine, and not obtained easily in ordinary clinical practice. This paper is to provide synopsis of the ante-disease diagno-therapeutics partly and to show predictable data based on the facial shapes and signs, especially of gall bladder's versus bladder's body and masculine versus feminine shape. Ante-disease means not only the complete healthy state, but also the state unseen any symptoms in macrographically in the course of outbreak of disease. It contains two stages, first one is the former state of disease and second one is untransmitted state of disease. The patterns of ante-disease consist of latent disease, pre-disease, transmission type like senescent syndrome, abnormal reactive syndrome(變證), syndrome of transmission and transmutation. The classification with gall bladder and bladder type manifests the differences of shape, color and size of each organ in comparison of the universal and standard figures of the human being. On the other hand, the classification with masculine and feminine shape contrasts the innate sexual difference and the shape, characteristics originated from in itself. These two classification theories have their own pathologic types and syndrome types with each disease so that disease-predictable data can be constructed based on such a relationship. In addition, this diagnostic method by facial shapes and signs is able to be applied to whole stages from prenatal to present state of disease even if the cause and inducement are not clear. Ante-disease diagno-theraputic system by Gall Bladder's versus Bladder's Body and Masculine versus Feminine Shape is getting more important in the chronic and internal disease in comparison of the acute and traumatic disease. So this study is able to make up for the limit of diagnosis on ante-disease in the field of oriental medicine clinic.

• 한국멀티미디어학회논문지
• /
• 제22권10호
• /
• pp.1168-1177
• /
• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• 정보관리학회지
• /
• 제35권2호
• /
• pp.63-88
• /
• 2018

본 연구는 LDA 토픽 모델과 딥 러닝을 적용한 단어 임베딩 기반의 Doc2Vec 기법을 활용하여 자질을 선정하고 자질집합의 크기와 종류 및 분류 알고리즘에 따른 분류 성능의 차이를 평가하였다. 또한 자질집합의 적절한 크기를 확인하고 문헌의 위치에 따라 종류를 다르게 구성하여 분류에 이용할 때 높은 성능을 나타내는 자질집합이 무엇인지 확인하였다. 마지막으로 딥 러닝을 활용한 실험에서는 학습 횟수와 문맥 추론 정보의 유무에 따른 분류 성능을 비교하였다. 실험문헌집단은 PMC에서 제공하는 생의학 학술문헌을 수집하고 질병 범주 체계에 따라 구분하여 Disease-35083을 구축하였다. 연구를 통하여 가장 높은 성능을 나타낸 자질집합의 종류와 크기를 확인하고 학습 시간에 효율성을 나타냄으로써 자질로의 확장 가능성을 가지는 자질집합을 제시하였다. 또한 딥 러닝과 기존 방법 간의 차이점을 비교하고 분류 환경에 따라 적합한 방법을 제안하였다.

• 음성과학
• /
• 제8권2호
• /
• pp.61-71
• /
• 2001

Speech material, which is collected from ARS(Automatic Response System), was analyzed and classified into disease and non-disease state. The material include 11 different kinds of diseases. Along with ARS speech, DAT(Digital Audio Tape) speech is collected in parallel to give the bench mark. To analyze speech material, analysis tools, which is developed local laboratory, are used to provide an improved and robust performance to the obtained parameters. To classify speech into disease and non-disease class, multi-layered neural network was used. Three different combinations of 3, 6, 12 parameters are tested to obtain the proper network size and to find the best performance. From the experiment, the classification rate of 92.5% was obtained.

• 농업과학연구
• /
• 제39권1호
• /
• pp.117-123
• /
• 2012

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.

• Journal of Yeungnam Medical Science
• /
• 제38권1호
• /
• pp.10-18
• /
• 2021

Endometriosis is a chronic disease associated with pelvic pain and infertility. Several classification systems for the severity of endometriosis have been proposed. Of these, the revised American Society for Reproductive Medicine classification is the most well-known. The ENZIAN classification was developed to classify deep infiltrating endometriosis and focused on the retroperitoneal structures. The endometriosis fertility index was developed to predict the fertility outcomes in patients who underwent surgery for endometriosis. Finally, the American Association of Gynecological Laparoscopists classification is currently being developed, for which 30 endometriosis experts are analyzing and researching data by assigning scores to categories considered important; however, it has not yet been fully validated and published. Currently, none of the classification systems are considered the gold standard. In this article, we review the classification systems, identify their pros and cons, and discuss what improvements need to be made to each system in the future.