의사결정나무분석과 로지스틱 회귀분석을 이용한 우울 예측요인 비교연구

윤지선 ( Youn Ji Sun ) 한국사회복지경영학회 2020 사회복지경영연구 Vol.7 No.2

본 연구는 노년기 삶의 질을 저해하는 우울증에 대한 관심으로부터 수행되었다. 의사결정나무(decision tree) 분석을 활용하여 노인의 우울 요인을 분류 및 예측하고, 이를 로지스틱 회귀분석 결과와 비교하여 예측 정확성을 정의하는 서술적 조사연구이다. 연구대상자는 국민연금연구원의 국민노후보장패널(KReIS) data 중, 7차 개인조사에 참가한 65세 이상 노인 총 2,096명이다. 자료분석은 SPSS 23.0 프로그램을 이용하여 기술통계, 교차분석, Roc Curve, 의사결정나무 분석, 로지스틱 회귀분석을 하였다. 연구결과, 의사결정나무 분석에서 우울 예측요인은 일상 및 사회생활 제한과 주관적 경제 불만족으로 나타났다. 로지스틱 회귀분석에서는 일상 및 사회생활 제한과 주관적 경제 불만족, 대인관계 불만족으로 나타났다. 노인의 우울에 영향을 미치는 예측력을 로지스틱 회귀분석과 의사결정나무 분석을 통해 비교한 결과, 우울을 예측하는 민감도는 로지스틱 회귀분석(44.4%)이 의사결정나무 분석(33.6%) 보다 높게 나타났다. 또한 실제 우울을 예측하는 특이도는 의사결정나무 분석(91.9%)이 로지스틱 회귀분석(86.3%) 보다 높은 것으로 나타났다. 분류정확도는 로지스틱 회귀분석(71.6%)이 의사결정나무 분석(71.4%)보다 조금 높게 나타났다. 연구결과를 기초로 두 기법의 예측 및 분류도 구로서의 유용성 판단은 민감도와 분류 정확도가 더 높게 나타난 로지스틱 회귀분석방법이 노인의 우울 예측모형을 구축하는데 더 유용한 자료로 사용될 수 있을 것으로 사료된다. 반면, 의사결정나무 분석은 분석의 정확도보다는 분석과정의 특정 경로설명이 필요한 경우에 유용하게 사용될 수 있을 것으로 보인다. This study was carried out from the interest in depression, which undermines the quality of life in old age, which has been extended by life expectancy. It is a descriptive investigation study that utilizes decision tree analysis with data mining technique to classify and predict depression factors of the elderly, and compare them with logistic regression results to define prediction accuracy. Among the data of the National Pension Research Institute's Korea National Age Security Panel(KReIS), a total of 2,096 senior citizens aged 65 or older participated in the seventh personal survey conducted in 2017. The data analysis was performed using the SPSS 23.0 program, including technical statistics, cross-analysis, logistic regression, Loc Curve, and decision tree analysis. The results of the study showed that the factors of depression prediction in decision tree analysis were daily and social life restriction and subjective economic dissatisfaction. Logistic regression showed limitations in daily and social life, subjective economic dissatisfaction and interpersonal dissatisfaction. Comparing the predictive power that affects the depression of the elderly through logistic regression and decision tree analysis, the sensitivity of predicting depression was higher than that of the decision tree(33.6%). In addition, the specificity of predicting actual depression was higher than that of logistic regression(86.3%) with decision tree analysis(91.9%). Classification accuracy was slightly higher than logistic regression(71.6%) in decision tree analysis(71.4%). Based on the results of the study, it is estimated that the logistic regression method, which shows higher sensitivity and accuracy of classification, can be used as more useful data to build a depression prediction model for the elderly. On the other hand, decision tree analysis may be useful when specific path descriptions of the analysis process are needed rather than the accuracy of the analysis.

기계학습 기반의 영화흥행예측 방법 비교: 인공신경망과 의사결정나무를 중심으로

권신혜,박경우,장병희 사단법인 인문사회과학기술융합학회 2017 예술인문사회융합멀티미디어논문지 Vol.7 No.4

본 연구는 영화산업의 가치사슬단계에 따라 각 단계에서 고려할 수 있는 변인을 활용하여 제작/투자, 배급, 상영단계별 모형을 구성하였다. 모형의 예측력을 높이기 위해 회귀분석으로 유의미한 변인을 도출하여 모형을 추가로 설정하였다. 주어진 변인을 바탕으로 기계학습 분석방법인 인공신경망과 의사결정나무 분석방법 간의 예측력 차이를 비교하였다. 분석 결과, 제작/투자 모형과 배급 모형에서 모든 변인을 투입했을 때는 인공신경망의 정확도가 의사결정나무보다 높았으나, 회귀분석결과에 따라 선정된 변인을 투입하였을 때는 의사결정나무의 정확도가 더 높았다. 상영 모형에서는 회귀분석결과의 반영여부와 관계없이 인공신경망의 정확도가 의사결정나무의 정확도보다 높게 나타났다. 본 논문은 영화흥행 예측연구에 기계학습기법을 적용하여 예측성과가 향상됨을 확인하였다는데 의의가 있다. 선형회귀분석 결과를 기계학습기법에 반영함으로써 기존의 선형적 분석방법의 한계를 극복하고자 하였다. In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

보건정보 자료를 이용한 나무구조회귀모형의 시각화에 대한 고찰

조현선 ( Hyun Sun Cho ),이은경 ( Eun Kyung Lee ) 한국보건정보통계학회(구 한국보건통계학회) 2020 보건정보통계학회지 Vol.45 No.1

Objectives: Among machine learning techniques, a tree-based regression model is widely used as easy to interpret and easy to use results. Methods: In this study, we examine the characteristics of regression models using various tree structures implemented in R, and apply them to public health data for visual representation and analysis to enhance understanding of the models and data. Results: We also look at the random forest, gradient boosting model, and xgboost that incorporate the results of various tree-structured model estimates using bagging and boosting, and visualize these estimation processes to compare the results of the model estimates. It also compares the performance of various tree-structured models using various public health data. It visually examines that the performance of the tree-structured models may vary depending on the characteristics of the data. Conclusions: Through this study, we apply the tree-structured models to public health data to enhance understanding of the data and to help identify and visualize the mechanism of the data.

공공 DB 데이터마이닝 기법을 활용한 국내 청소년 삶의 만족도 분석에 관한 실증연구: 의사결정나무 기법을 중심으로

조현진,고건우,이건창 한국디지털정책학회 2020 디지털융복합연구 Vol.18 No.6

본 연구는 국내 공공 DB에 데이터마이닝 기법인 로지스틱 회귀분석과 의사결정나무 분석을 적용하여 국내 청소년의 삶의 만족도 증진에 관한 의미 있는 의사결정 규칙을 추출하는 과정을 분석한다. 분석을 위하여 한국아동·청소년패널조사(KYCPS) 중에서 중1 패널데이터의 4~6차연도 자료인 고등학생 학년별 자료를 활용하였다. 로지스틱 회귀분석으로 추출된 영향요인은 1학년은 전체 성적 만족도, 주의집중 문제, 우울, 자아 탄력성, 애정, 과잉간섭, 학습활동, 교사 관계, 2학년은 가정의 경제 수준, 건강상태, 전체 성적 만족도, 신뢰, 소외, 학습활동, 학교규칙, 교우관계, 교사 관계, 3학년은 가정의 경제 수준, 전체 성적 만족도, 우울, 자아 탄력성, 애정, 학대, 학교규칙, 교사 관계로 나타났다. 의사결정나무 기법을 적용한 결과 국내 고등학생의 삶의 만족도는 개인의 정서 문제, 학교성적, 가정의 경제적 환경, 학교적응 등에 의하여 복합적으로 영향을 받는 것으로 파악되었다. This study focuses on the application of the data mining technique logistic regression analysis and decision tree analysis to the domestic public database called Korean Children Youth Panel Survey (KCYPS) to derive a series of important factors affecting the enhancement of life satisfaction of domestic youth. As a result, the general impact factors on life satisfaction for each grade were derived from logistic regression. Using decision tree analysis, we came to conclusions that those factors such as depression, overall grade satisfaction, household economic level, and school adaptation play crucial roles in affecting high school adolesscents’ life satisfaction.

Chrysanthemums morifolium의 유전적 다양성 분석과 표현형질의 평가

인병천,심성철,임진희 한국화훼학회 2015 화훼연구 Vol.23 No.4

Chrysanthemum (Chrysanthemum morifolium) is one of the most popular ornamental species in the world due to the great diversity of flower color and flower head type. There has been increasing demands for various types of chrysanthemums, such as cut flowers, potted plants and bedding plants. In this work, we investigated genetic diversity in 60 commercial chrysanthemum cultivars using simple sequence repeats (SSRs) and examined the relationship between clustering data and the phenotypic characteristics of chrysanthemum flowers. Cluster analysis based on 38 phenotypic traits showed that most of the chrysanthemum cultivars were separated into 8 groups according to flower color and flower head type. Of the 150 SSR primer pairs tested in this study, 62 primers were obtained from previous studies, while 88 primers were designed using the unigene sequences of C. nankingense and the Expressed Sequence Tag (EST) sequences of C. morifolium in the NCBI database. Thirteen SSR primers were selected based on polymorphism and banding patterns in a subset of 8 cultivars and used to amplify the DNA of 60 chrysanthemum cultivars. A cluster analysis based on these 13 SSR markers showed that all 60 chrysanthemum cultivars were divided into six clusters according to their flower color. To determine the relationship between the phylogenetic tree and flower color, multiple regression analysis (MRA) was performed with flower color as the dependent variable and SSR markers as the independent variables. The MRA results revealed a highly significant relationship (r2 = 0.903, P < 0.05) between the flower color and the SSR markers. These results will benefit chrysanthemum research community to develop elite cultivars. 국화(Chrysanthemum morifolium)는 다양한 화색과 화형 때문에 세계에서 가장 인기 있는 관상식물 중 하나로서 절화, 분화 및 화단용 등 다양한 형태의 국화에 대한 요구가 증가하고 있다. 본 연구는 SSR 마커를 이용하여 국화 60 품종에 대한 유전적 유연관계를 조사하고, 군집분석결과와 표현형간의 상관관계를 조사하기 위하여 수행하였다. 표현형질 38개를 이용한 군집분석 결과, 대부분의 국화 품종들이 화형과 화색에 따라 8개의 그룹으로 분류되는 것으로 나타났다. 본 연구에서 사용된 150 개의 SSR 프라이머는 기존연구에서 보고된 62개와 C.nankingense의 unigene 염기서열 및 C. morifolium의EST 염기서열로부터 디자인한 88개로 구성되었다. 국화8품종에 대한 다형성 및 banding pattern 결과를 토대로하여 국화 60 품종의 DNA 증폭에 사용할 13개의 SSR 마커를 최종 선발하였다. SSR 마커를 이용하여 군집분석을 행한 결과, phylogenetic tree에서 국화 60 품종 전부가 화색에 따라서 6개의 그룹으로 분류되는 것을 확인할 수 있었다. Phylogenetic tree와 화색간의 상관관계를 조사하기 위하여 화색을 종속변수, SSR 마커를 독립변수로 설정한 다중회귀분석(MRA)을 행하였다. MRA 결과는 화색과 SSR 마커간에 통계적 유의성이 높은 상관관계(r2 = 0.903, P < 0.05)를 나타냈다. 본 연구결과는 경쟁력 있는 국화 신품종 육종을 위한 데이터로 활용될 수있을 것으로 생각된다.

로지스틱 회귀분석과 의사결정나무 분석을 통한 노인의 인지기능장애 예측요인 비교분석: 디지털 리터러시 변인을 중심으로

노미영 ( Roh Mi-young ) 미래융합통섭학회 2024 현대사회와 안전문화 Vol.7 No.1

연구목적 본 연구는 로지스틱 회귀분석과 의사결정나무 분석을 활용하여 노인의 디지털 리터러시에 초점을 두고 인지 기능장애 예측요인을 규명하고자 한다. 연구방법 대상은 2020년도 노인실태조사 자료에 기초해 65세 이상 노인 9,920명이었다. 분석은 로지스틱 회귀분석과 의사결정나무 분석을 시행하였다. 결과 첫째, 로지스틱 회귀분석에서 인지기능장애를 예측하는 주요한 요인으로 디지털 리터러시, 친목 활동, 교육수준, IADL 제한, 연령, 우울, 성별, 가구형태로 나타났다. 둘째, 로지스틱 회귀분석과 의사결정나무 분석의 공통 요인으로 디지털 리터러시 연령, 교육수준, 주관적 건강상태, 우울, 친목 활동으로 나타났다. 셋째, 로지스틱 회귀분석과 의사결정나무 분석 결과, 각각 특이도 84.2%와 85.3%, 민감도 52.5%와 47.9%, 정확도 73.2%와 72.5%로 나타나 모두 높은 예측 정확도를 보였다. 결론 노인의 인지기능장애를 예방하기 위하여 디지털 리터러시가 낮은 노인의 연령대를 고려하여 친목 활동을 독려하는 프로그램을 개발하고 평가하는 연구의 필요성을 제언한다. PURPOSE The purpose of this study was to identify factors that can predict cognitive impairment in the elderly, focusing on digital literacy, through logistic regression (LR) and decision tree analysis (DT). METHOD The subjects were 9,920 elderly people from the 2020 survey on the welfare and living conditions of the elderly. Data analysis was performed using LR and DT analysis. RESULT First, in the LR analysis, the main factors predicting cognitive impairment were digital literacy, social activities, education, IADL limitation, age, depression, gender, and living arrangement. Second, common predicting factors of cognitive impairment were digital literacy, age, education, subjective health status, depression, and social activities in both analyses. Third, the model accuracy results of the LR and DT analysis showed specificity 84.2% and 85.3%, sensitivity 52.5% and 47.9% and accuracy 73.2% and 72.5%, respectively, and both showed high prediction accuracy. CONCLUSION To prevent cognitive impairment, it is necessary to develop and evaluation interventions that encourage social activities by considering age group of the elderly with low digital literacy.

국방 C5ISR 분야 품질문제의 빅데이터 분석 및 예측 모델에 대한 연구

허형조,고수진,백승현 한국품질경영학회 2023 품질경영학회지 Vol.51 No.4

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

리조트 豫約不渡에 影響을 미치는 決定要因에 대한 探索的 推定

尹設玟(Yoon, Seol-Min) 청운대학교 관광산업연구소 2013 관광산업연구 Vol.7 No.1

The purpose of this study is to estimate explanatory variables of no-show to the resort using the logistic regression analysis and decision tree analysis. Based on literature review and specialist opinion investigation this study included the experience of no-show in domestic accommodations, the cause of no-show in resort, the experience of cancellation in resort, demographic characteristics, and the experience(1=yes, 0=no) of no-show in resort as a dependent variable. The results of logistic regression analysis and decision tree analysis indicate that the experience made no-show in domestic accommodations except resort, and the cancellation experience to resort are statistically significant when determining the experience of no-show to resort. According to importance among variables explaining the experience of no-show in resort, the experience of no-show in domestic accommodations except resort is most important. Managerial implications for the prevention of no-show to resort management are presented in the conclusion section.

CART분석을 이용한 교통사고예측모형의 개발

이재명,김태호,이용택,원제무 한국도로학회 2008 한국도로학회논문집 Vol.10 No.1

Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. The accurate traffic accident prediction model requires not only understanding of the factors that cause the accident but also having the transferability of the model. So, this paper suggest the traffic accident diagram using CART(Classification And Regression Tree) analysis. developed Model is compared with the existing accident prediction models in order to test the goodness of fit. The results of this study are summarized below. First, traffic accident prediction model using CART analysis is developed. Second, distance(km), pedestrian shoulder(m) and traffic volume among the geometrical factors are the most influential to the traffic accident. Third. CART analysis model show high predictability in comparative analysis between models. This study suggest the basic ideas to evaluate the investment priority for the road design and improvement projects of the traffic accident blackspots.

한국관광 실태조사 빅 데이터 분석을 통한 관광산업 활성화 방안 연구

이정미(Jungmi Lee),류미나(Meina Liu),임규건(Gyoo Gun Lim) 한국지능정보시스템학회 2018 지능정보연구 Vol.24 No.2

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the Government 3.0. Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the Survey on the actual situation of foreign tourists from 2013 to 2015 surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the Satisfaction, Revisit intention, and Recommendation variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing ‘overall satisfaction’ were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the Government 3.0. Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher β value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher β value than other variables. From this analysis, we found that ‘food satisfaction’ and ‘sightseeing spot attraction’ variables were the common factors to influence three dependent variables that are mentioned above(‘Overall satisfaction’, ‘Revisit intention’ and ‘Recommendation’), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used