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  • Title/Summary/Keyword: LOGISTIC REGRESSION ANALYSIS

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Influencing factors of using Korean Medicine services - focusing on the 2017 Korean Medicine Utilization Survey (한방의료이용 선택 요인에 관한 연구 - 2017 한방의료이용실태조사를 중심으로)

  • Lim, Jinwoong;Lee, Kee-Jae
    • The Journal of Korean Medicine
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    • v.42 no.1
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    • pp.12-25
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    • 2021
  • Objectives: The aim of this study was to investigate influencing factors of using Korean medicine services (KMS) using the 2017 Korean Medicine Utilization Survey (KMUS). Methods: Demographic statistics of the survey were summarized and influencing factors of the KMS experience and the intention to visit KMS were analyzed using logistic regression model with complex sample design. Influencing factors were specified based on Andersen's behavioral model of health care utilization and factors associated with individual recognitions of KMS. Additionally, using the ordinary logistic regression model without complex sample design, the survey data were analyzed to compare the results. Results: In the logistic regression analysis, sex, age, health condition, presence of chronic disease, a degree of knowledge about Korean Medicine, and a view about herbal medicine safety were statistically significant both in the KMS experience, and the intention to visit KMS. Marital status was statistically significant in the KMS experience, while family income, a view about the cost of KMS were statistically significant in the intention to visit KMS. Conclusion: Individual recognitions of KMS and enabling components should be considered when establishing KMS policies. In addition, future studies analyzing KMUS need to take into account the complex sample design features of the survey to avoid statistically misleading results.

Exploring the Impact of Pesticide Usage on Crop Condition: A Causal Analysis of Agricultural Factors

  • Mee Qi Siow;Yang Sok Kim;Mi Jin Noh;Mu Moung Cho Han
    • Smart Media Journal
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    • v.12 no.10
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    • pp.29-37
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    • 2023
  • Human lifestyle is affected by the agricultural development in the last 12,000 years ago. The development of agriculture is one of the reasons that global population surged. To ensure sufficient food production for supporting human life, pesticides as a more effective and economical tools, are extensively used to enhance the yield quality and boost crop production. This study investigated the factors that affect crop production and whether the factors of pesticide usage are the most important factors in crop production using the dataset from Kaggle that provides information based on crops harvested by various farmers. Logistic regression is used to investigate the relationship between various factors and crop production. However, the logistic regression is unable to deal with predictors that are related to each other and identifying the greatest impact factor. Therefore, causal discovery is applied to address the above limitations. The result of causal discovery showed that crop condition is greatly impacted by the estimated insects count, where estimated insects count is affected by the factors of pesticide usage. This study enhances our understanding of the influence of pesticide usage on crop production and contributes to the progress of agricultural practices.

A Study on the Insolvency Prediction Model for Korean Shipping Companies

  • Myoung-Hee Kim
    • Journal of Navigation and Port Research
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    • v.48 no.2
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    • pp.109-115
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    • 2024
  • To develop a shipping company insolvency prediction model, we sampled shipping companies that closed between 2005 and 2023. In addition, a closed company and a normal company with similar asset size were selected as a paired sample. For this study, data of a total of 82 companies, including 42 closed companies and 42 general companies, were obtained. These data were randomly divided into a training set (2/3 of data) and a testing set (1/3 of data). Training data were used to develop the model while test data were used to measure the accuracy of the model. In this study, a prediction model for Korean shipping insolvency was developed using financial ratio variables frequently used in previous studies. First, using the LASSO technique, main variables out of 24 independent variables were reduced to 9. Next, we set insolvent companies to 1 and normal companies to 0 and fitted logistic regression, LDA and QDA model. As a result, the accuracy of the prediction model was 82.14% for the QDA model, 78.57% for the logistic regression model, and 75.00% for the LDA model. In addition, variables 'Current ratio', 'Interest expenses to sales', 'Total assets turnover', and 'Operating income to sales' were analyzed as major variables affecting corporate insolvency.

Predicting Land Use Change Affected by Population Growth by Integrating Logistic Regression, Markov Chain and Cellular Automata Models

  • Nguyen, Van Trung;Le, Thi Thu Ha;La, Phu Hien
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.35 no.4
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    • pp.221-230
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    • 2017
  • Demographic change was considered to be the most major driver of land use change although there were several interacting factors involved, especially in the developing countries. This paper presents an approach to predict the future land use change using a hybrid model. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Experiment was conducted in Giao Thuy district, Nam Dinh Province, Vietnam. Demography and socio-economic variables dealing with urban sprawl were used to create a probability surface of spatio-temporal states of built-up land use for the years 2009, 2019, and 2029. The predicted land use maps for the years 2019 and 2029 show substantial urban development in the area, much of which are located in areas sensitive to source protections. It also showed that aquacultural land changes substantially in areas where are in the vicinity of estuary or near the sea dike. There was considerable variation between the communes; notably, communes with higher household density and higher proportion of people in working age have larger increases in aquacultural areas. The results of the analysis can provide valuable information for local planners and policy makers, assisting their efforts in constructing alternative sustainable urban development schemes and environmental management strategies.

Nomogram comparison conducted by logistic regression and naïve Bayesian classifier using type 2 diabetes mellitus (T2D) (제 2형 당뇨병을 이용한 로지스틱과 베이지안 노모그램 구축 및 비교)

  • Park, Jae-Cheol;Kim, Min-Ho;Lee, Jea-Young
    • The Korean Journal of Applied Statistics
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    • v.31 no.5
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    • pp.573-585
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    • 2018
  • In this study, we fit the logistic regression model and naïve Bayesian classifier model using 11 risk factors to predict the incidence rate probability for type 2 diabetes mellitus. We then introduce how to construct a nomogram that can help people visually understand it. We use data from the 2013-2015 Korean National Health and Nutrition Examination Survey (KNHANES). We take 3 interactions in the logistic regression model to improve the quality of the analysis and facilitate the application of the left-aligned method to the Bayesian nomogram. Finally, we compare the two nomograms and examine their utility. Then we verify the nomogram using the ROC curve.

Development of Hypertension Predictive Model (고혈압 발생 예측 모형 개발)

  • Yong, Wang-Sik;Park, Il-Su;Kang, Sung-Hong;Kim, Won-Joong;Kim, Kong-Hyun;Kim, Kwang-Kee;Park, No-Yai
    • Korean Journal of Health Education and Promotion
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    • v.23 no.4
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    • pp.13-28
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    • 2006
  • Objectives: This study used the characteristics of the knowledge discovery and data mining algorithms to develop hypertension predictive model for hypertension management using the Korea National Health Insurance Corporation database(the insureds' screening and health care benefit data). Methods: This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques. Results: Major results of logistic regression analysis suggested that the probability of hypertension was: - lower for the female(compared with the male)(OR=0.834) - higher for the persons whose ages were 60 or above(compared with below 40)(OR=4.628) - higher for obese persons(compared with normal persons)(OR= 2.103) - higher for the persons with high level of glucose(compared with normal persons)(OR=1.086) - higher for the persons who had family history of hypertension(compared with the persons who had not)(OR=1.512) - higher for the persons who periodically drank alcohol(compared with the persons who did not)(OR=1.0371.291) Conclusions: This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation's building of a Hypertension Management System in the near future by bringing forth representative results on the rise and care of hypertension.

A Study on Accident Prediction Models for Chemical Accidents Using the Logistic Regression Analysis Model (로지스틱회귀분석 모델을 활용한 화학사고 사상사고 예측모형 개발 연구)

  • Lee, Tae-Hyung;Park, Choon-Hwa;Park, Hyo-Hyeon;Kwak, Dae-Hoon
    • Fire Science and Engineering
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    • v.33 no.6
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    • pp.72-79
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    • 2019
  • Through this study, we developed a model for predicting chemical accidents lead to casualties. The model was derived from the logistic regression analysis model and applied to the variables affecting the accident. The accident data used in the model was analyzed by studying the statistics of past chemical accidents, and applying independent variables that were statistically significant through data analysis, such as the type of accident, cause, place of occurrence, status of casualties, and type of chemical accident that caused the casualties. A significance of p < 0.05 was applied. The model developed in this study is meaningful for the prevention of casualties caused by chemical accidents and the establishment of safety systems in the workplace. The analysis using the model found that the most influential factor in the occurrence of casualty in accidents was chemical explosions. Therefore, there is an urgent need to prepare countermeasures to prevent chemical accidents, specifically explosions, from occurring in the workplace.

Analysis of Decision Factors on the Participation of Scaling Project for Private Forest Management using a Logit Model (로짓모형을 이용한 산주의 사유림 경영 규모화 사업 참여 결정요인 분석)

  • Kim, Ki Dong
    • Journal of Korean Society of Forest Science
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    • v.105 no.3
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    • pp.360-365
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    • 2016
  • The purpose of this study is to provide the basic information for the early enforcement and extension of the improvement project of management scale of private forest land by understanding the characteristics of forest owners, who have an influence on the participation of the project as one of the private forest management vitalization plans. To achieve this goal, a questionnaire survey targeting 373 forest owners was conducted and analyzed by Binary-Logistic Regression. The variables for binary-logistic regression included gender, age, academic ability, occupation, income, residence, purpose of forest ownership, and status of cooperative membership. As a result of the analysis, 267 forest owners (71.6%) of total 373 forest owners have the intention to participate in the scaling project for private forest management. The rest of forest owners (106 forest owners, 28.4%) would not be willing to participate in the project. As a result of binary-logistic regression, the most important variables, which have an impact on the participation of private forest management scale improvement project, are age, job and forest own purpose.

Multi-currencies portfolio strategy using principal component analysis and logistic regression (주성분 분석과 로지스틱 회귀분석을 이용한 다국 통화포트폴리오 전략)

  • Shim, Kyung-Sik;Ahn, Jae-Joon;Oh, Kyong-Joo
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.1
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    • pp.151-159
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    • 2012
  • This paper proposes to develop multi-currencies portfolio strategy using principal component analysis (PCA) and logistic regression (LR) in foreign exchange market. While there is a great deal of literature about the analysis of exchange market, there is relatively little work on developing trading strategies in foreign exchange markets. There are two objectives in this paper. The first objective is to suggest portfolio allocation method by applying PCA. The other objective is to determine market timing which is the strategy of making buy or sell decision using LR. The results of this study show that proposed model is useful trading strategy in foreign exchange market and can be desirable solution which gives lots of investors an important investment information.

Machine learning in survival analysis (생존분석에서의 기계학습)

  • Baik, Jaiwook
    • Industry Promotion Research
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    • v.7 no.1
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    • pp.1-8
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    • 2022
  • We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (ti, δi) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.