Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.

Emerging trend of big data analytics in bioinformatics: a literature review

Purpose: This study was done to investigate the factors which influence depressive symptoms in older people. Methods: This was a cross-sectional study. Data were collected through questionnaires from 393 older people. Data analysis was done with SPSS/WIN 17.0. Results: The prevalence of depression for elders was 14.8%. Living with family and level of education were significant factors related to depressive symptoms identified through multiple logistic regression analysis. After adjusting for several confounding factors, the factors related to depressive symptoms were number of chronic diseases, social support, frequency of social contact, sleep disturbance, and stress. Conclusion: Results of this study indicate that there is a need to construct and implement an integrated preventive program for depression which deals with care for chronic disease, reduction of stress, increased connection with a community network for social support and management of sleep disturbances.

https://www.jkpmhn.org/Synapse/Data/PDFData/0199JKAPMHN/jkapmhn-22-107.pdf

Factors Influencing Depressive Symptoms in Community Dwelling Older People

Objectives: The purpose of this study was to identify its influencing factors of depression in middle aged women. Methods: We analyzed cross-sectional data collected from 142 middle aged women by using self-reported questionnaire Korean version of Center for Epidemiologic Studies Depression Scale (CES-D), Menopause-specific Quality of life (MENQOL), Korean Perceived Stress Scale-10 (KPSS-10), Multiple dimensional Scale of Perceived Social Support (MSPSS), and Perceived health status. Data were analyzed stepwise multiple regression with SPSS 19.0. Results: The mean score for depression was 17.45. There were difference of depression scores in marital satisfaction (F = 6.15, p= 0.001) and quality of sleep (F = 8.12, p≤ 0.001). There were difference of menopausal specific quality of life that were statistically significant in marital satisfaction (F = 5.40, p= 0.002) and quality of sleep (F = 4.20, p= 0.003). Depression was correlated with stress (r = 0.69, p< 0.001), social support (r = 0.52, p< 0.001), menopausal specific quality of life (r = 0.52, p< 0.001) and perceived health status (r = -0.44, p< 0.001). The factors related to depression were stress (β = 0.48, p< 0.001), social support (β = -0.25, p< 0.001), quality of life on menopause (β = 0.18, p< 0.001) which explained 56% of the variance reported in depression. Conclusions: The results indicate that these factors influencing depression should be considered when developing integrated intervention program to cope with depression in middle aged women.

/pdf/factors-influencing-depression-in-middle-aged-women-focused-49eecm3177.pdf

Factors Influencing Depression in Middle Aged Women: Focused on Quality of life on Menopause

In medical field, massive data sets is generated by rapid development of hospital information system. For analysis of these medical big data, this study showed analysis process of the clinical data to acquire significant information effectively between the foot disorder groups and biomechanical parameters related to symptom by developing a prediction model of the decision tree. The first clinical health records of 1523 patients diagnosed with foot disorder were used for analysis, in total 6610 records. The dependent variable in the analysis data was consisted of five complex disorder groups, and the independent variable was composed of 24 attributes. The decision tree was applied to analyze pattern of the foot disorder. The measured prediction rate was Correct: 72.96 % and Wrong: 27.04 % in the training data, and Correct: 68.66 % and Wrong: 31.34 % in the test data. As a result of analysis on the five foot complex foot disorder groups by using C5.0 algorithm, 12 rules were generated. To improve accuracy of classification, the detailed preprocessing and other data mining algorithms will be applied from now on.

Application of big data analysis with decision tree for the foot disorder

Objectives The purpose of this study was to design a prediction model that explains the characteristics of elderly adults at risk of malnutrition. Methods Data were obtained from a large data set, 2008 Korean Elderly Survey, in which the data of 15,146 subjects were entered. With nutritional status a target variable, the input variables included the demographic and socioeconomic status of participants. The data were analyzed by using the SPSS Clementine 12.0 program's feature selection node to select meaningful variables. Results Among the C5.0, C&R Tree, QUEST, and CHAID models, the highest predictability was reported by C&R Tree with the accuracy rate of 77.1%. The presence of more than two comorbidities, living alone status, having severe difficulty in daily activities, and lower perceived economic status were identified as risk factors of malnutrition in elderly. Conclusions A reliable decision support model was designed to provide accurate information regarding the characteristics of elderly individuals with malnutrition. The findings demonstrated the good feasibility of data mining when used for a large community data set and its value in assisting health professionals and local decision makers to come up with effective strategies for achieving public health goals.

/pdf/knowledge-discovery-in-a-community-data-set-malnutrition-2wrazjc4l7.pdf

Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly

Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

/pdf/analysis-of-the-characteristics-of-the-older-adults-with-55bc4wthwl.pdf

Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis

본 연구의 목적은 청년의 정부 청년정책 신뢰에 관련된 인구·사회학적 변인들을 파악하고, 청년의 청년정책에 대한 인식, 청년정책 기대수준, 청년취업 공정수준이 정부 청년정책 신뢰에 미치는 영향력을 분석함으로써 적실성 있는 청년정책 방향을 제시하는 것이다. 우선 연구대상자들의 사회·인구학적 특성을 분석하고, 사용 척도의 신뢰도를 담보하기 위하여 Cronbach α계수를 파악하였다. 나이, 경제적 수준, 청년정책에 대한 인식, 청년정책 기대수준, 청년취업 공정수준, 정부 청년정책 신뢰에 대한 남녀 간 차이를 살펴보기 위하여 t-test를 실시하였다. 그리고 선택한 변인들이 정부 청년정책 신뢰에 미치는 영향을 분석하기 위하여 위계적 회귀분석을 실시하였다. 연구결과를 살펴보면, 첫째, 청년들의 경제적 수준, 청년정책 기대수준, 정부 청년정책에 대한 신뢰 인식 정도에서 남녀 간 차이가 유의미하게 나타났다. 둘째, 청년정책에 대한 인식 수준이 정부 청년정책 신뢰에 유의한 영향력을 가지는 것으로 나타났다. 셋째, 청년취업 공정수준 인식이 정부 청년정책 신뢰에 추가적인 영향력과 조절력을 가지는 것으로 나타났다. 

Analysis of Factors Affecting Youth’s Confidence Level in Government Youth Policy: Focusing on the Relationship between Socio-Demographic Characteristics, Youth’s Perception of Youth Policy, Youth Policy Expectation Level, and Youth Employment Fair Level

본 연구는 위기청소년 지원 서비스에 대한 탐색적 분석을 통해 향후 관련 정책 추진을 위한 정책 개선방안을 제시하는 것을 목적으로 한다. 위기청소년 지원 서비스에 대한 다각적 분석을 위해 위기청소년 당사자 및 부모를 대상으로 초점집단면접(FGI)을 실시하고, 그 결과로 얻어진 텍스트 데이터에 대해 토픽분석 방법을 적용하여 분석을 시도하였다. 연구결과를 통한 정책적 제언은 다음과 같다. 첫째, 청소년들은 주요한 위기 경험의 하나로 진학, 취업 등 진로와 미래에 대한 어려움이 주요 문제이므로 각 위기 유형별 특성을 고려해 전문적인 개입과 지원이 제공되어야 한다. 둘째, 위기청소년 지원기관 운영과 지원서비스 제공에 있어 위기청소년의 특성과 생활양식, 활동공간을 고려하는 맞춤형 정책이 필요하다. 셋째, 청소년의 위기 경험을 예방하거나 조기 개입을 위해서는 위기가정에 대해 보다 적극적이고 선제적인 지원이 필요하다. 넷째, 위기청소년에 대한 효과적인 지원과 대책을 위해서는 위기청소년 지원기관 간 협력과 통합적 지원을 강화하는 것이 필요하다. 마지막에 본 연구의 제약점과 후속연구를 위한 제언을 기술하였다. 

An Exploratory Study on At-Risk Youth Support Services

본 연구는 초등아동 대상의 공적 돌봄서비스 이용경험을 다각적으로 분석하여 향후 관련 정책 추진을 위한 개선방안을 제시하는 것을 목적으로 한다. 초등아동 대상의 공적 돌봄서비스 이용경험 분석을 위해 서울시 교육지원청 및 학교장, 담임교사, 지역사회 돌봄서비스 기관 담당자의 협조를 통해 초등학생 자녀가 있는 학부모 또는 주양육자 652명을 대상으로 온라인 설문조사를 실시하였다. 연구결과를 통한 제언은 다음과 같다. 첫째, 초등돌봄과 관련하여 방학 기간 중 평일에 돌봄 공백이 많으므로 방학 기간 아동 돌봄에 대한 서비스 확대가 필요하다. 둘째, 공적 돌봄 지원 정책 수립 시 모든 맞벌이 가구의 일-가정 양립 실태와 욕구에 대한 적극적인 고려가 필요하다. 셋째, 공적 돌봄서비스를 활성화하기 위해서는 공적 돌봄 이용 자격 기준에 대한 탄력적 검토가 필요하다. 넷째, 공적 돌봄 대신 사교육을 진행하는 이유는 돌봄 공백을 보완하기 위한 것으로 나타나 공적 서비스의 실질적 확충이 필요한 상황이다. 다섯째, 향후 공적 돌봄 자원과 공급을 확대하고 돌봄의 공공성을 강화하기 위해서는 공적 돌봄 제공 시간대의 확대 및 조정이 빠르게 이루어져야 할 것이다. 

Chul Hoi Koo

Papers

Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis

Analysis of Factors Affecting Youth’s Confidence Level in Government Youth Policy: Focusing on the Relationship between Socio-Demographic Characteristics, Youth’s Perception of Youth Policy, Youth Policy Expectation Level, and Youth Employment Fair Level

An Exploratory Study on At-Risk Youth Support Services

A Study on the Experience and Improvement of Public Care Services for Elementary School Children