• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.97-99
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• 2021

최근 고령화 사회가 지속됨에 따라, 치매(Dementia)에 대한 관심이 높아지고 있다. 그 중에서 알츠하이머병(Alzheimer's disease)는 전체 치매 환자의 50~60%로 가장 많은 비율을 차지하는 퇴행성 뇌질환으로, 현재 의료계에선 알츠하이머병에 대한 명확한 예방법 및 치료법에 대해 내놓지 못하고 있으며, 치매 발병 전 조기 치료 및 조기 예방법에 대한 중요성이 강조되고 있다. 본 논문에서는 정상인과 알츠하이머병에 걸린 환자의 MRI 데이터셋을 활용하여 컨볼루션 신경망을 중심으로 여러 가지 활성화 함수를 접목시켜, 가장 효율적인 활성화 함수를 찾고자 한다. 또한 알츠하이머 치매분류 모델링을 통해 향후 의료분야에 적합한 치매 구분 모델링으로 활용하고자 한다.

• Parasites, Hosts and Diseases
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• 제59권6호
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• pp.595-605
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• 2021

An understanding of the knowledges, attitudes and perceptions of different populations is key for public health policy makers. Here, a survey was performed on knowledge, attitudes, and perceptions about malaria diagnosis, prevention, control, and treatment. The 407 survey participants included both uninfected inhabitants and patients from 2 cities (Gimpo- and Paju-si) of Northern Gyeonggi-do, known as high-risk areas for vivax malaria. We used community-based study design and non-probability sampling method using the primary data. Association between variables were tested using χ²-tests. In general, the information on malaria reported by the participants in this study was unsystematic and included inaccurate details. The knowledge of malaria symptoms, identified as headache, chills and fever, was high, but the surveyed community lacks knowledge of the specific medications used for malaria treatment, with a large number of respondents having no knowledge of any form of medication. Survey questions with high correct answer rates included questions about easy treatment of malaria in Korea, the high daytime activity of malaria-borne mosquitoes, and the infection risk posed by outdoor activities. However, a large portion of the respondents was unable to provide simple medical and biological information about the disease. This study aimed to comprehensively evaluate the knowledge, attitude, and practical behavior of the surveyed community with respect to malaria and the implications reported here could be applicable to other malaria endemic areas in Korea.

• Annals of Clinical Neurophysiology
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• 제23권1호
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• pp.7-16
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• 2021

Transcranial magnetic stimulation (TMS) is a safe and noninvasive tool for investigating the cortical excitability of the human brain and the neurophysiological functions of GABAergic, glutamatergic, and cholinergic neural circuits. Neurophysiological biomarkers based on TMS parameters can provide information on the pathophysiology of dementia, and be used to diagnose Alzheimer's disease and differentiate different types of dementia. This review introduces the basic principles of TMS, TMS devices and stimulating paradigms, several neurophysiological measurements, and the clinical implications of TMS for Alzheimer's disease.

• 한국축산식품학회지
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• 제42권6호
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• pp.981-995
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• 2022

Cognitive dysfunction is a common symptom of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and Huntington's disease, and is known to be caused by the structural and functional loss of neurons. Many natural agents that can improve cognitive function have been developed and assessed for efficacy using various cognitive deficit animal models. As the gut environment is known to be closely connected to brain function, probiotics are attracting attention as an effective treatment target that can prevent and mitigate cognitive deficits as a result of neurodegenerative diseases. Thus, the objective of this review is to provide useful information about the types and characteristics of cognitive deficit animal models, which can be used to evaluate the anti-cognitive effects of probiotics. In addition, this work reviewed recent studies describing the effects and treatment conditions of probiotics on cognitive deficit animal models. Collectively, this review shows the potential of probiotics as edible natural agents that can mitigate cognitive impairment. It also provides useful information for the design of probiotic treatments for cognitive deficit patients in future clinical studies.

• 한국치위생학회지
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• 제17권5호
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• pp.747-755
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• 2017

Objectives: The purpose of study is to investigate periodontal disease-related recognition and oral health-related behavior in orthodontic patients with fixed appliance. Methods: A self-reported questionnaire was completed by 286 orthodontic patients with fixed appliance in Gwangju, Jeonnam from September 1 to September 27, 2016. The questionnaire consisted of general characteristics (3 items), orthodontic related characteristics (3 items), knowledge of periodontal disease (3 items), and oral health-related behavior (4 items). The data were analyzed by frequency analysis, percentage and chi-square analysis using SPSS 21.0 program. Results: 62.8% had experiences of dental treatment and 67.5% had intention of involvement on incremental care program in orthodontic treatment periods. Accuracy rate of cause about periodontal disease was high in female and case of acquiring information experiences on periodontal disease (p<0.05). 67.2% performed correct toothbrushing for the management of periodontal disease in the experiences of acquiring information on periodontal disease in orthodontic treatment periods (p<0.05). The proportions of using interdental toothbrush and mouth rinsing solutions were high among those over 20 years old and students in the subjects (p<0.05). Conclusions:The accuracy rate were high in the answers about cause and management of periodontal disease in case of acquiring information experiences on periodontal disease in orthodontic treatment periods. Therefore, there is a need to further development and implementation of dental hygiene intervention program for periodontal disease care with fixed orthodontic appliances in that regard.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권10호
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• pp.493-498
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• 2017

캡슐내시경 검사는 일반적인 내시경의 접근이 어려운 소장을 관찰하는 데 특화되어 있다. 캡슐내시경 검사를 통한 진단 과정은 크게 적응증 판단, 내시경 검사, 진단의 세 단계로 이루어진다. 이 때, 진단을 위해 필요한 핵심 의료 정보로는 적응증, 병변, 질환 정보가 있다. 본 논문에서는 이와 같은 핵심 정보를 의미적 특징 정보, 이를 추출하는 과정을 의미 기반 분석이라 정의한다. 이와 같은 의미 기반 분석은 내시경 검사 전 과정에 걸쳐 수행된다. 먼저 캡슐내시경 검사에 앞서 환자의 증상을 확인하여 예상 질병 정보를 획득한다. 다음, 획득한 정보를 기반으로 캡슐내시경 검사를 실시한 후 발견된 병변의 위치와 진단을 위한 조직, 혈관, 산도와 같은 보조 정보들을 활용하여 최종 진단을 내린다. 이때, 예상 질병을 확인하기 위한 증상과 질병 간의 연관성이나 병변의 위치로부터 확인해야할 보조 정보 간의 해부학적 연관성이 고려되어야 한다. 그러나 기존의 내시경 관련 의료 정보 표준과 같은 지식 모델은 단순히 내시경 검사와 관련된 용어들이 나열된 형태로 의미적 연관성이 고려되지 않는다. 따라서 본 논문에서는 캡슐내시경 검사의 진단 보조를 위한 의미적 연관성 기반의 지식 모델을 제안한다. 제안하는 모델은 캡슐내시경 검사의 주요 대상 기관인 소장에 특화된 질병 모델과 해부학 모델로, 캡슐내시경 검사를 위한 효과적인 의료 정보 제공을 가능케 한다.

• BMB Reports
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• 제42권6호
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• pp.356-360
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• 2009

In the present study we have examined human-mouse homologous intronless disease and non-disease genes alongside their extent of sequence conservation, tissue expression, domain and gene ontology composition to get an idea regarding evolutionary and functional attributes. We show that selection has significantly discriminated between the two groups and the disease associated genes in particular exhibit lower $K_{a}$ and $K_{a}/K_{s}$ while $K_{s}$ although smaller is not significantly different. Our analyses suggest that majority of disease related intronless human genes have homology limited to eukaryotic genomes and their expression is localized. Also we observed that different classes of intronless disease related genes have experienced diverse selective pressures and are enriched for higher level functionality that is essentially needed for developmental processes in complex organisms. It is expected that these insights will enhance our understanding of the nature of these genes and also improve our ability to identify disease related intronless genes.

• Korean Journal of Acupuncture
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• 제28권4호
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• pp.159-167
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• 2011

Objectives : This study was performed to find a desirable way for acupuncture treatment of Alzheimer Disease. Methods : The authors reviewed several literatures about 'Alzheimer Disease' and 'Dementia'. Based on the review, we interpreted the causes and symptoms on viewpoint of three yin and three yang, and also researched desirable way for acupuncture treatment of Alzheimer Disease. Results and Conclusions : The symptoms of Alzheimer Disease belong to the unbalance between reverting yin and lesser yang. The factors affecting attack rate of Alzheimer Disease are also related with the unbalance between reverting yin and lesser yang. It is necessary to inhibit the function of reverting yin and to activate the function of lesser yang for the acupuncture treatment of Alzheimer Disease.

• 스마트미디어저널
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• 제13권6호
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.