• 대한지역사회영양학회지
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• 제4권3호
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• pp.421-430
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• 1999

A neural network system was applied in order to analyze the nutritional and other factors influencing chronic diseases. Five different nutrition evaluation methods including SD Score, %RDA, NAR INQ and %RDA-SD Score were utilized to facilitate nutrient data for the system. Observing top three chronic disease prediction ratio, WHR using SD Score was the most frequently quoted factor revealing the highest predication rate as 62.0%. Other high prediction rates using other data processing methods are as follows. Prediction rate with %RDA, NAR, INQ and %RDA-SD Score were 58.5%(diabetes), 53.5%(hyperlipidemia), 51.6%(diabetes), and 58.0%(diabetes)respectively. Higher prediction rate was observed using either NAR or INQ for obesity as 51.7% and 50.9% compared to the previous result using SD Score. After reviewing appearance rate for all chronic disease and for various data processing method used, it was found that iron and vitamin C were the most frequently cited factors resulting in high prediction rate.

• 한국인공지능학회지
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• 제11권1호
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• pp.1-7
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• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

• 한국식물병리학회지
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• 제14권5호
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• pp.433-439
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• 1998

The studies on the ecology of virus disease on Chinese cabbage (Brassica campestris subsp. pekinensis) cultivated in alpine area of Kangwon province during summer season to analyse its influence on damage and develope a prediction model were performed from 1993 to 1997. Virus disease on Chinese cabbage occurring in the alpine area showed various symptom types and among there, necrotic spots and dwarf were mainly detected. The disease was increased from early August and continued mid September in every year. The occurrence of virus disease was the highest in 1994 with 20.5%, and the number of aphid vectors were also the highest during the same period. The number of aphids in the alpine areas showed twice peaks every year. For the analysis of damage by virus infection, the infection and injured ratio of all treatments were more than 90% and 80%, respectively. The most important factor for the occurrence of virus disease on Chinese cabbage was temperature. Factors influencing the development of the viral disease in the alpine area were maximum temperature and number of aphid vectors.

• 한국정보통신학회논문지
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• 제26권5호
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• pp.755-762
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• 2022

본 논문에서는 학습 예측이 가능한 군집적 알고리즘으로 COVID-19에서 상황인식정보인 질병의 속성정보와 클러스터링를 이용한 군집적 알고리즘을 제안한다. 클러스터링 내에서 처리되는 군집 데이터는 신규 또는 새롭게 입력되는 정보가 상호관계를 예측하기 위해 분류 제공되는데, 이때 새롭게 입력되는 정보가 비교정보에서 오염된 정보로 처리되면 기존 분류된 군집으로부터 벗어나게 되어 군집성을 저하시키는 요인으로 작용하게 된다. 본 논문에서는 COVID-19에서의 질병속성 정보내 K-means알고리즘을 이용함에 있어 이러한 문제를 해결하기 위해 질병 상호관계 정보 추출이 가능한 사용자 군집 분석 방식을 제안하고자 한다. 제안하는 알고리즘은 자율적인 사용자 군집 특징의 상호관계를 분석학습하고 이를 통하여 사용자 질병속성간에 따른 클러스터를 구성해 사용자의 누적 정보로부터 클러스터의 중심점을 제공하게 된다. 논문에서 제안된 COVID-19의 다중질병 속성정보군집단위로 분류하고 학습하는 알고리즘은 적용한 모의실험 결과를 통해 사용자 관리 시스템의 예측정확도가 학습과정에서 향상됨을 보여주었다.

• 대한의용생체공학회:의공학회지
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• 제31권5호
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• pp.375-384
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• 2010

The purpose of this study is to determine whether there is a correlation between the segmental bio-impedance measurement with the frequency modulations and the life-style disease of obesity. An obesity is not simply the factor for estimating the life-style disease of obesity, but also the risk factor occurring. There are many methods (BMI, WHR, Waist, CT, DEXA, BIA, etc.) for measuring a degree of obesity; the bio-impedance measurement is more economic and more effective than others. The physical examination, the blood test, the medical imaging diagnosis and the bio-impedancemeasurementswithmultiple frequencies for each body parts have been conducted for 77 people. The estimated value has been calculated through a segmental bio-impedance model based on multi-frequency that was created to reflect the highest correlation by analyzing correlation with linear regression analysis method for the measured bio-impedance and the risk factors. Then we compared with the clinical diagnosis. In case of high level cholesterol, low HDL-C and high LDL-C for life-style disease, the sensitivity is 80~100%and the specificity is 83~100%. This study has shown conclusively that bio-impedance can be a possible predictor to analyze the disease risk rate of population and individual health maintenance. And also the multi-frequency segmental bio-impedance can be used as early predictor to estimate the life-style disease of obesity.

• Journal of Information Technology Applications and Management
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• 제27권6호
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• pp.171-180
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• 2020

With the recent increase in asthma, asthma has become recognized as one of the diseases. The perception that bronchial asthma is a chronic disease and requires treatment has been strengthened. In addition, asthma is recognized as a dangerous disease due to environmental changes and efforts are made to minimize these risks. However, the environmental impact on asthma is hardly a factor that individuals in asthmatic patients can cope with. Therefore, this study was conducted to see if the asthma disease could be replaced by the individual efforts of asthma patients. In particular, since the management of asthma is important during adolescence, we conducted research on asthma in teenagers. Utilizing support vector machines, artificial neural networks and deep learning techniques that have recently drawn attention, we propose models to predict the asthma of teenagers. The study also provides guidelines to avoid factors that can cause asthma in teenagers.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.39-46
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• 2019

Human mobility estimation plays a key factor in a lot of promising applications including location-based recommendation systems, urban planning, and disease outbreak control. We study the human mobility estimation problem in the case where recent locations of a person-of-interest are unknown. Since matrix decomposition is used to perform latent semantic analysis of multi-dimensional data, we propose a human location estimation algorithm based on matrix factorization to reconstruct the human movement patterns through the use of information of persons with correlated movements. Specifically, the optimization problem which minimizes the difference between the reconstructed and actual movement data is first formulated. Then, the gradient descent algorithm is applied to adjust parameters which contribute to reconstructed mobility data. The experiment results show that the proposed framework can be used for the prediction of human location and achieves higher predictive accuracy than a baseline model.

• Asian Pacific Journal of Cancer Prevention
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• 제13권5호
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• pp.1841-1844
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• 2012

Background: The majority of patients with pancreatic cancer present with advanced disease. Systemic chemotherapy has limited impact on overall survival (OS) so that eligible patients should be selected carefully. The aim of this study was to analyze prognostic factors for survival in Turkish advanced pancreatic cancer patients who survived more than one year from the diagnosis of recurrent and/or metastatic disease and receiving gemcitabine (Gem) alone or gemcitabine plus cisplatin (GemCis). Methods: This retrospective evaluation was performed for patients who survived more than one year from the diagnosis of recurrent and/or metastatic disease and who received gemcitabine between December 2005 and August 2011. Twenty-seven potential prognostic variables were chosen for univariate and multivariate analyses to identify prognostic factors associated with survival. Results: Among the 27 variables in univariate analysis, three were identified to have prognostic significance: sex (p = 0.04), peritoneal dissemination (p =0.02) and serum creatinine level (p=0.05). Multivariate analysis by Cox proportional hazard model showed only peritoneal dissemination to be an independent prognostic factor for survival. Conclusion: In conclusion, peritoneal metastasis was identified as an important prognostic factor in metastatic pancreatic cancer patients who survived more than one year from the diagnosis of recurrent and/or metastatic disease and receiving Gem or GemCis. The findings should facilitate pretreatment prediction of survival and can be used for selecting patients for treatment.

• 문화기술의 융합
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• 제6권4호
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• pp.289-294
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• 2020

기능성 위장장애는 Helicobacter pylori 감염 및 비 스테로이드성 항염증제의 사용 등의 원인으로 발생하는 소화기 계통 질환이다. 그동안 기능성 위장장애의 위험요인에 대한 많은 연구들이 수행되어졌으나, 한국인에 대한 기능성 위장장애 예측 모델 제시에 대한 연구는 없는 실정이다. 따라서 본 연구의 목적은 중년 및 노년층을 대상으로 인구학적정보, 비만정보, 혈액정보, 영양성분 정보를 바탕으로 머신러닝을 이용하여 기능성위장장애 예측 모델을 구현하고 평가하는 것이다. 모델생성을 위해 wrapper-based variable selection 메소드와 naive Bayes 알고리즘이 사용되었다. 여성 예측 모델의 분류 정확도는 0.712의 the area under the receiver operating characteristics curve(AUC) 값을 나타냈고, 남성에서는 여성보다 낮은 0.674의 AUC값이 나타났다. 이러한 연구결과는 향후 중년 및 노년층의 위장장애 질환의 예측과 예방에 활용될 수 있다.

• 한국인터넷방송통신학회논문지
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• 제23권4호
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• pp.127-135
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• 2023

요즘, 우울증 및 스트레스로 자살하는 환자가 급증하고 있다. 뿐만 아니라, 스트레스 및 우울증이 오래 지속되면, 심장병 및 뇌 질환, 고혈압 등을 유발할 수 있는 위험한 요소로 질환이다. 그러나, 아무리 현대 의학이 발전하였지만, 우울증 및 심장병 환자에게는 특별한 약이나 치료제가 없는 매우 난감한 상황이다. 그러므로, 세계 여러 나라에서, 심전도 및 산소포화도, 뇌파 분석 기능을 이용해서 우울증 위험환자 및 자살 위험환자를 조기에 판단하는 연구가 활발하게 이루어지고 있다. 본 논문에서는, 이러한 문제점을 분석하기 위해서, 심장병 가설데이터를 수립해서, 심장병 위험환자를 판단하는 컴퓨터 모의실험을 수행하였다. 특히, 심장병 발생 예측을 을 10% 이상 향상하게 시키기 위해서, 퍼지 추론을 사용하는 모의실험을 수행하였다.