• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.515-527
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• 2005

The data mining is a new approach to extract useful information through effective analysis of huge data in numerous fields. We utilized this data mining technique to analyze medical record of 39,900 people. Whole data were separated by gender first and divided into three groups, including normal, stage 1 hypertension, and stage 2 hypertension. The data from each group were analyzed with data mining technique. Based on the result that we have extracted with this data mining technique, major risk factors for the hypertension are age, BMI score, family history.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.217-227
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• 2005

The data mining is a new approach to extract useful information through effective analysis of huge data in numerous fields. We utilized this data mining technique to analyze medical record of 35,671 people. Whole data were assorted by BMI score and divided into two groups. We tried to find out BMI risk factor from overweight group by analyzing the raw data with data mining approach. The result extracted by C5.0 decision tree method showed that important risk factors for BMI score are triglyceride, gender, age and HDL cholesterol. Odds ratio of major risk factors were calculated to show individual effect of each factors.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.361-363
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• 2001

Although data mining promises a new paradigm to discover medical knowledge form a database, there are many problems to be solved before real application is feasible. We had the chance to provide a data set to be analyzed as a discovery challenge by using various data mining techniques at the PKDD conference. As data providers, we evaluated and discussed results and clarified problems.

• 서비스연구
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• 제1권1호
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• pp.61-69
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• 2011

데이터 마이닝은 대용량의 데이터베이스로부터 기존에 알려지지 않은, 즉 단순한 질의어로 추출할 수 없는 형태의 '유용한' 정보를 찾아내고 이를 바탕으로 데이터에 대한 통찰(insight)을 얻는 것으로 정의할 수 있다. 본 논문에서는 웹에서 발생하거나 웹 사이트에 저장한 데이터를 대상으로 유용한 패턴을 찾아내기 위하여 인터넷을 이용하는 이용자의 특징을 분석하기 위해 시도되었다. 즉 인터넷 사용자에 대한 일반적인 통계 정보 데이터에 연관성 분석을 적용하여 인터넷 사용 시간에 영향을 미치는 인터넷 이용자의 특징을 분석하였다. 실험을 통하여 데이터로부터의 연관 규칙을 추출 해내었으며, 최적의 결과를 도출하기위한 데이터 전처리 및 알고리즘을 적용하여 웹 마이닝을 위한 인터넷 사용자의 특징을 분석한 결과 그 유용성을 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2006년도 춘계종합학술대회
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• pp.1039-1042
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• 2006

최근 들어 고객정보의 체계적인 분석과 고객의 다양한 패턴을 발견하고 분석 및 예측을 하기 위한 목적으로 많은 기업들이 eCRM을 빠르게 도입하고 있고 과거에 주로 사용되던 통계적 과정을 자동화하여 일반인들도 쉽게 양질의 결과를 추출하고 예측 할 수 있는 데이터마이닝으로 점점 대체되고 있는 추세이다. 이러한 데이터마이닝이 대표적으로 이용되고 있는 분야가 eCRM이다. 본 논문에서는 A할인점의 고객 데이터와 1년간의 매출 데이터를 기반으로 데이터마이닝을 동해 이듬해 고객기여도를 예측하는 실험을 하여 실제 데이터와 예측된 데이터와의 비교를 통해 데이터마이닝이 eCRM에 얼마나 효과적인지를 입증하였다.

• 치위생과학회지
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• 제21권1호
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• pp.70-78
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• 2021

Background: The aim of this study was to analyze the public demand for information about the job of dental hygienists by mining text data collected from the online Q & A section on an Internet portal site. Methods: Text data were collected from inquiries that were posted on the Naver Q & A section from January 2003 to July 2020 using "dental hygienist job recognition," "role recognition," "medical assistance," and "scaling" as search keywords. Text mining techniques were used to identify significant Korean words and their frequency of occurrence. In addition, the association between words was analyzed. Results: A total of 10,753 Korean words related to the job of dental hygienists were extracted from the text data. "Chi-lyo (treatment)," "chigwa (dental clinic)," "ske-illing (scaling)," "itmom (gum)," and "chia (tooth)" were the five most frequently used words. The words were classified into the following areas of job of the dental hygienist: periodontal disease treatment and prevention, medical assistance, patient care and consultation, and others. Among these areas, the number of words related to medical assistance was the largest, with sixty-six association rules found between the words, and "chi-lyo," "chigwa," and "ske-illing" as core words. Conclusion: The public demand for information about the job of dental hygienists was mainly related to "chi-lyo," "chigwa," and "ske-illing" as core words, demonstrating that scaling is recognized by the public as the job of a dental hygienist. However, the high demand for information related to treatment and medical assistance in the context of dental hygienists indicates that the job of dental hygienists is recognized by the public as being more focused on medical assistance than preventive dental care that are provided with job autonomy.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• 디지털융복합연구
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• 제18권7호
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• pp.299-306
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• 2020

본 연구는 환자중심성 의료문화 변화에 따라, 데이터마이닝 기법을 이용한 융복합 외래 의료서비스 환자경험조사 연구를 시행하여 환자중심성 의료기관 경영전략에 도움이 될 수 구체적 방안을 모색하고자 하였다. '2018 의료서비스경험조사' 원시자료를 이용하여 외래 의료서비스 환자경험이 있는 만 15세 이상 8,843명을 분석하였다. 의사결정나무분석을 수행하였다. 외래 의료서비스 환자경험에 대한 전반적 만족도 결정요인은 의사와 환자 권리보호였으며 추천의사 결정요인은 의사와 시설의 안락함과 편안함이었다. 여성이 남성에 비해 전반적 만족도에서 경험을 긍정적으로 평가했으며 60세 이상이 전반적 만족도와 추천의사에 대한 경험을 긍정적으로 평가했다. 외래 의료서비스 환자경험 의사결정예측 모형을 제시하고 의사 영역과 환자권리보호 영역, 시설의 안락함과 편안함이 중요한 요인임을 확인한 점이 의의가 있다. '의료서비스경험조사'에 대한 종단적 연구가 필요하며 입원 의료서비스경험에 대한 연구가 필요하다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제61차 동계학술대회논문집 28권1호
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• pp.271-272
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• 2020

Data mining is known useful in medical area when no availability of evidence favoring a particular treatment option is found. Huge volume of structured/unstructured data is collected by the healthcare field in order to find unknown information or knowledge for effective diagnosis and clinical decision making. The data of 5,179 records considered for analysis has been collected from Korean National Health and Nutrition Examination Survey(KHANES) during 2-years. Data splitting, referred as the training and test sets, was applied to predict to fit the model. We analyzed to predict chronic kidney disease (CKD) using data mining method such as naive Bayes, logistic regression, CART and artificial neural network(ANN). This result present to select significant features and data mining techniques for the lifestyle factors related CKD.