• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.354-360
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• 2022

Metabolic disease is a disease with a prevalence of 26% in Korean, and has three of the five states of abdominal obesity, hypertension, hunger glycemic disorder, high neutral fat, and low HDL cholesterol at the same time. This paper links the consumer panel data of the Rural Development Agency(RDA) and the medical care data of the National Health Insurance Service(NHIS) to generate a classification model that can be divided into a metabolic disease group and a control group through food consumption characteristics, and attempts to compare the differences. Many existing domestic and foreign studies related to metabolic diseases and food consumption characteristics are disease correlation studies of specific food groups and specific ingredients, and this paper is logistic considering all food groups included in the general diet. We created a classification model using regression, a decision tree-based classification model, and a classification model using XGBoost. Of the three models, the high-precision model is the XGBoost classification model, but the accuracy was not high at less than 0.7. As a future study, it is necessary to extend the observation period for food consumption in the patient group to more than 5 years and to study the metabolic disease classification model after converting the food consumed into nutritional characteristics.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.75-80
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• 2018

In this paper, we propose heterogeneous lifelog mining model in health big-data platform. It is an ontology-based mining model for collecting user's lifelog in real-time and providing healthcare services. The proposed method distributes heterogeneous lifelog data and processes it in real time in a cloud computing environment. The knowledge base is reconstructed by an upper ontology method suitable for the environment constructed based on the heterogeneous ontology. The restructured knowledge base generates inference rules using Jena 4.0 inference engines, and provides real-time healthcare services by rule-based inference methods. Lifelog mining constructs an analysis of hidden relationships and a predictive model for time-series bio-signal. This enables real-time healthcare services that realize preventive health services to detect changes in the users' bio-signal by exploring negative or positive correlations that are not included in the relationships or inference rules. The performance evaluation shows that the proposed heterogeneous lifelog mining model method is superior to other models with an accuracy of 0.734, a precision of 0.752.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.413-420
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• 2019

The purpose of the present study is to develop a model for predicting hypercholesterolemia using an integrated set of body fat mass variables based on machine learning techniques, beyond the study of the association between body fat mass and hypercholesterolemia. For this study, a total of six models were created using two variable subset selection methods and machine learning algorithms based on the Korea National Health and Nutrition Examination Survey (KNHANES) data. Among the various body fat mass variables, we found that trunk fat mass was the best variable for predicting hypercholesterolemia. Furthermore, we obtained the area under the receiver operating characteristic curve value of 0.739 and the Matthews correlation coefficient value of 0.36 in the model using the correlation-based feature subset selection and naive Bayes algorithm. Our findings are expected to be used as important information in the field of disease prediction in large-scale screening and public health research.

• Annual Conference of KIPS
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• 2011.11a
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• pp.858-861
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• 2011

Smart Society로 나아가는 핵심으로 손꼽히는 모바일의 급속한 확산은 교육, 교통, 경제뿐만 아니라 건강에도 큰 영향을 미치고 있다. 그동안 발전이 미진했던 u-Health 기술 및 시장 역시 모바일로 인해 새로운 서비스 모델을 제시함으로써 발전하고 있다. 본 논문은 제안하는 시스템은 u-Health 서비스 중 sensor를 이용하여 원격지 환자의 생체정보를 수집하고, 실시간으로 병원의 클라우드 서버에 전송하는 시스템에서 사용자 검증에 대하여 연구하였다. 여기서 사용자란 클라우드 서버에 접속하는 의사를 말하며, 환자의 생체정보를 보기 위하여 시스템 접속함에 있어 공인인증서나 기타 인증 시스템과 비교해 간편하고, 네트워크 트래픽이 적은 사용자 검증 시스템을 목표로 한다. 그리하여 QRcode를 3개 생성하고, 각 클라우드 서버에 분산 분배 후 서로 섞음으로써 기존의 QRcode와 전혀 다른 인증용 QRcode를 생성할 수 있었다. 이것을 3차원 인덱스를 통해 원본 사용자 QRcode와 대조함으로 사용자 검증 과정을 수행시킴으로써 절차를 간소화하고 네트워크 트래픽을 약 15% 감소시킬 수 있었다.

• Journal of Food Hygiene and Safety
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• v.14 no.4
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• pp.386-396
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• 1999

Commonly eaten fish, meat and other protein-containing foods show some level of mutagenic activity following normal cooking such as broiling, frying, grilling, roasting etc. The main food mutagens found in cooked products are“heterocyclic aromatic amines”. Several of them have been shown to be carcinogenic in rodent and suggested to be relevant for human cancer etiology. This review summarizes the chemistry, formation, occurrence and toxicity of food-borne heterocyclic aromatic amines. Factors that influence the formation of them are also discussed with special emphasis on dietary factors. From a health safety point of view, it is desirable to estimate the intake of heterocyclic amines via foods, and reduce or prevent the formation of food mutagens.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.11-16
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• 2019

People are subject to time sanctions in a busy modern society. Therefore, people find it difficult to eat simple junk food and even exercise, which is bad for their health. As a result, the incidence of chronic diseases is increasing. Also, the importance of making accurate and appropriate inferences to individual characteristics is growing due to unnecessary information overload phenomenon. In this paper, we propose a knowledge reasoning model using association rules and cluster analysis of multi-contexts. The proposed method provides a personalized healthcare to users by generating association rules based on the clusters based on multi-context information. This can reduce the incidence of each disease by inferring the risk for each disease. In addition, the model proposed by the performance assessment shows that the F-measure value is 0.027 higher than the comparison model, and is highly regarded than the comparison model.

• Annual Conference of KIPS
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• 2022.05a
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• pp.452-455
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• 2022

4차 산업혁명의 확산으로 의학계에서도 딥러닝 기술을 이용한 질병의 치료결과 예측 연구가 활발하다. 이와 관련, 일부 연구에서 국소적인 환자 데이터의 활용으로 인해 도출된 연구 결과의 일반화가 어려웠으며 예측률 제고를 위해 특정 딥러닝 알고리즘을 중심으로 한 실험이 추진되어 다양한 알고리즘별 예측률의 비교·분석 결과를 제시하는 연구도 미흡하였다. 이에, 건강보험심사평가원의 대규모 진료 정보와 다종의 알고리즘을 제공하는 AutoML을 이용, 사망률이 높은 80대·90대 노령자 대상 폐암 진단 후 84개월간의 사망률을 예측하는 Decision Tree 등 5개 알고리즘별 모델을 생성하고 이를 활용, 사망률의 예측 성능을 비교하고 사망률에 영향을 미치는 요인에 대한 분석 결과를 도출하였다.

• Journal of Life Science
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• v.33 no.2
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• pp.183-190
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• 2023

Red yeast rice, also known as Hong Qu and red Koji, has been used for a long time in Asian functional food and traditional medicine. It consists of multiple bioactive substances, which can potentially be used as nutraceuticals. Alcoholic liver disease (ALD) can range from simple steatosis or inflammation to fibrosis and cirrhosis, possibly through leaky gut and systemic endotoxemia. This study examined the liver and gut effects of red yeast rice (RYR) (Monascus purpureus) ethanol extract against binge ethanol-induced liver injury in mice. RYR extract was orally administered to C57BL/6N mice at a concentration of 200 mg/kg body weight per day for 10 days. Then, mice were administered binge alcohol (5 g/kg/dose) three times at 12 hr intervals. Binge alcohol exposure significantly elevated the endotoxin, aspartate aminotransferase (AST), and alanine transaminase (ALT) activity of plasma, as well as hepatic triglyceride levels; however, RYR treatments reduced these levels. In addition, RYR pretreatment significantly reduced the alcohol-induced oxidative maker protein and apoptosis maker in binge alcohol-induced gut and liver injuries. These results suggest that RYR may prevent alcohol-induced acute leaky gut and liver damage.

• Journal of Life Science
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• v.32 no.5
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• pp.355-361
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• 2022

Melanin pigments are the main cause of skin color. They are produced in melanocytes and then transferred to keratinocytes, which eventually gives the skin surface a variety of colors. Although many skin-lightening or depigmenting agents have been developed, the demand for materials to reduce pig- mentation is still increasing. Here, we tried to find materials for skin-lightening or depigmentation using natural compounds and found that mistletoe (Viscum album var. coloratum) extracts (ME) had an inhibitory effect on tyrosinase activity. As a result, ME significantly reduced pigmentation in human primary melanocytes. In addition, a promoter reporter assay revealed that ME inhibited the transcription of microphthalmia-associated transcription factor (MITF), melanophilin (MLPH), tyrosinase-related protein-2 (TRP-2), and tyrosinase (TYR) genes in HM3KO melanoma cells. In addition, ME decreased the protein level for pigmentation-related molecules, such as TYR and TRP-1. Furthermore, it markedly inhibited the melanogenesis of zebrafish embryos, an in vivo evaluation model for pigmentation. To elucidate the action mechanism of ME, we investigated its effects on intracellular signaling. Eventually, the ME dramatically decreased the phosphorylation of the cAMP responsive element binding protein (CREB), AKT, and ERK. The data suggest that ME may inhibit the melanogenesis pathway by regulating the signaling pathway related to pigmentation. Taken together, these data propose that ME can be developed as a depigmenting or skin-lightening agent.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.38-47
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• 2021

This study is about a blockchain-based collective quarantine management system and its commercialization model. The configuration of this system includes a biometric information transmission unit that generates biometric information based on measured values generated from wearable devices, a biometric information transmission unit that transmits biometric information generated here from a quarantine management platform, and action information transmitted from the community server. is a system including an action information receiving unit for receiving from the quarantine management platform. In addition, a biometric information receiving unit that collects biometric information from the terminal, an encryption unit that encodes biometric information generated through the biometric information receiving unit based on blockchain encryption technology, and a database of symptoms of infectious diseases to store symptom information and an infection diagnosis database. The generated database includes a location information check unit that receives from the terminal of the user identified as a symptomatic person and determines whether the user has arrived in the community based on the location information confirmation unit and the location of the user after the location is confirmed. It includes a community arrival judgment unit that judges. And, the community server helps the interaction between the generated information. Such a blockchain based collective quarantine management system can help to advance the existing quarantine management system and realize a safer and healthier society.