• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.321-324
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• 2023

본 연구는 광역시와 광역도 간의 개인적 요인과 건강수준 정도가 우울경험 여부에 영향을 미치는 변수의 중요도를 파악하고자 시도되었다. 본 연구의 자료는 질병관리청의 2021년 지역사회건강조사 데이터를 활용하였다. 광역시의 데이터는 4,602건을 이용하였고, 광역도는 19,545건의 데이터를 이용하였다. 자료 분석에 활용된 빅데이터는 R 4.3.0 for Windows를 활용하여 단어 빈도 분석과 machine learning기법인 Random Forest분석을 실시하였다. 연구결과, train 데이터와 test 데이터의 과적합(overfitting)의 문제는 발생하지 않았으며, machine learning 기법의 분류모델은 약 94% 수준으로 나타났다. 분석 결과 광역시와 광역도 간의 우울경험여부에 미치는 중요도가 각각 다르게 나타났다. 두 지역의 시민에게 미치는 우울경험의 원인을 다르게 접근함으로써 보다 더 효율적인 정책수립이 가능 할 것으로 판단된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.835-841
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• 2018

In this study, we analyzed public health data analysis using the hadoop-based spack in the cloud environment using the data of the Community Health Survey from 2012 to 2014, and the factors affecting the quality of life and quality of life. In the proposed paper, we constructed a cloud manager for parallel processing support using Hadoop - based Spack for open medical big data analysis. And we analyzed the factors affecting the "quality of life" of the individual among open medical big data quickly without restriction of hardware. The effects of public health data on health - related quality of life were classified into personal characteristics and community characteristics. And multiple-level regression analysis (ANOVA, t-test). As a result of the experiment, the factors affecting the quality of life were 73.8 points for men and 70.0 points for women, indicating that men had higher health - related quality of life than women.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1351-1356
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• 2019

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic patients based on the Big Data Platform. As a method of study, second data of 2017 community health survey and Statistics Korea by City·Gun·Gu public office were used and a multi-level analysis was conducted after separating EQ-5D index, individual factor and community factor. As a result, men, age, education level, monthly household income, having economic activity, the number of sports infrastructure were positively associated with the quality of life, and subjective health not good, extremely perceived stress were negatively associated with the quality of life. Research will continue to provide a platform independent of hardware that can utilize the cloud and open source for medical big data analysis in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.282-285
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• 2018

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic diseases based on the Big Data Platform. The research methodology was the matching of the 2017 Community Health Survey data and the National Statistical Office data to the health center units. In the study, The higher the age, the higher the education level, the higher the monthly household income, the economic activity, the spouse, the higher the quality of life. In the case of community factors, the lower the population density, the lower the elderly population ratio, the more doctors engaged in medical institutions, the higher the financial independence, the higher the quality of life.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1277-1286
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• 2018

In this paper, we provide flexible scalability of computing resources in cloud environment and Apache Hadoop based cloud environment for analysis of public medical information big data. In fact, it includes the ability to quickly and flexibly extend storage, memory, and other resources in a situation where log data accumulates or grows over time. In addition, when real-time analysis of accumulated unstructured log data is required, the system adopts Hadoop-based analysis module to overcome the processing limit of existing analysis tools. Therefore, it provides a function to perform parallel distributed processing of a large amount of log data quickly and reliably. Perform frequency analysis and chi-square test for big data analysis. In addition, multivariate logistic regression analysis of significance level 0.05 and multivariate logistic regression analysis of meaningful variables (p<0.05) were performed. Multivariate logistic regression analysis was performed for each model 3.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.895-903
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• 2018

The purpose of this study is to conduct a comparative analysis on the mental health and health-related quality of life of the community residents who have been affected by the Sewol ferry disaster and to provide a basis for future aids for potential human-caused catastrophe. The study utilized the big data, Korean community health survey data of 3,632 (1,803 in 2018 and 1,829 in 2014) residents in Ansan and Jindo, a victimized community of Sewol ferry disaster, and compared the subjective level of health, amount of appropriate sleep, subjective stress level, stress consulting, depression, depression consulting, and health-related quality of life before and after the disaster. According to the findings, community residents who have been affected by the Sewol ferry disaster did not show any significant difference in terms of subjective health level, amount of appropriate sleep, subjective stress, and health-related quality of life. The study aims to serve as a foundational data for developing a prevention program or policy that aims to support future potential disasters by comparing the mental health and health-related quality of life of the victimized community before and after the disaster. The study suggests that the need for continuous monitoring of mental health and health-related quality of life of the victimized residents is significant, and further studies should be implemented in order to provide better supports for the local community.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.68-78
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• 2017

The purpose of this study was to identify the influence of the individual-level and community-level factors in the ecological model on walking and to provide the basic data for a strategy that can increase walking for health promotion of adult workers. By combining the primary data of community health survey (CHS) (2011-2013) with the Korea national statistics annual book (2011-2013), the regional level variables were extracted from 253 municipal districts and the convergent big data with the hierarchical structure was produced. As a result, the increase in budget expenditure for public order and safety in social and cultural environment factors, the increase in budget expenditure for national and community land development in the leisure environment factors, and the number of buses in the transportation environment were increased by walking. In conclusion, walking was increased by the development of a community environment and bus transportation besides individual characteristics and behavior. Therefore, improving environment and public transportation will increase physical activity, such as walking, which will increase the health expectancy in community citizen workers.

• Journal of Industrial Convergence
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• v.21 no.3
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• pp.99-106
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• 2023

The purpose of this study was to identify the factors affecting depression according to the causes of suicidal ideation. The data used the 2021 Community Health Survey data. The participants of the study were 5,328 adults between the ages of 20 and 60 who responded that they had suicidal thoughts in the past year. For the analysis of the data, a composite sample analysis was performed using the SPSS 25.0 program. The results of the study are as follows. 1) In the economic difficulties group, age, gender, education level, economic activity, job change due to COVID-19, life satisfaction, subjective health status, stress, sleep time, and annual unmet medical care were the main factors related to depression. 2) In the interpersonal problem group, age, gender, education level, economic activity, life satisfaction, subjective health status, smoking, drinking, stress, and sleeping time were the main factors associated with depression. 3) In the disease and disability group, age, marital status, education level, life satisfaction, smoking, stress, sleep time, and annual unmet medical care were the main influencing factors of depression. Therefore, in order to reduce the rate of suicide and prevent depression, it is necessary to establish various strategies according to the causes of suicidal ideation and the influencing factors of depression.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.581-588
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• 2017

This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.728-739
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• 2020

Using webometric big data, this study examines the diversity of online information sources about the novel coronavirus causing the COVID-19 pandemic. Specifically, it focuses on some 28 countries where confirmed coronavirus cases occurred in February 2020. In the results, the online visibility of Australia, Canada, and Italy was the highest, based on their producing the most relevant information. There was a statistically significant correlation between the hit counts per country and the frequency of visiting the domains that act as information channels. Interestingly, Japan, China, and Singapore, which had a large number of confirmed cases at that time, were providing web data related to the novel coronavirus. Online sources were classified using an N-tuple helix model. The results showed that government agencies were the largest supplier of coronavirus information in cyberspace. Furthermore, the two-mode network technique revealed that media companies, university hospitals, and public healthcare centers had taken a positive attitude towards online circulation of coronavirus research and epidemic prevention information. However, semantic network analysis showed that health, school, home, and public had high centrality values. This means that people were concerned not only about personal prevention rules caused by the coronavirus outbreak, but also about response plans caused by life inconveniences and operational obstacles.