• Journal of the Korea Safety Management & Science
• /
• v.9 no.4
• /
• pp.29-38
• /
• 2007

We adapted association rules of data mining in order to investigate the relation among the factors of musculoskeletal disorders and proposed the method of preventing the musculoskeletal disorders associated with multiple logistic regression in previous study. This multiple logistic regression was difficult to establish the method of preventing musculoskeletal disorders in case factors can't be managed by worker himself, i.e., age, gender, marital status. In order to solve this problem, we devised association rules of factors of musculoskeletal disorders and proposed the interactive method of preventing the musculoskeletal disorders, by applying association rules with the result of multiple logistic regression in previous study. The result of correlation analysis showed that prevention method of one part also prevents musculoskeletal disorders of other parts of body.

• Health Policy and Management
• /
• v.4 no.2
• /
• pp.58-76
• /
• 1994

In order to study factors affecting the use of the ambulatory physician services. Andersen's model for health utilization was modified by adding the health behavior component and examined with three different approaches. Three different approaches were the multiople regression model, logistic regression model, and LISREL model. For multiple regression, dependent variable was reported illness-related visits to a physician during past one year and independent variables are variaous variables measuring predisposing factor, enabling factor, need factor and health behavior. For the logistic regression, dependent variable was visit or no-visit to a physician during past one year and independent variables were same as the multiple regression analysis. For the LISREL, five endogenous variables of health utiliztion, predisposing factor, enabling factor, need factor, and health behavior and 20 exogeneous variables which measures five endogenous variables were used. According to the multiple regression analysis, chronic illness, health status, perceived health status of the need factor; residence, sex, age, marital status, education of the predisposing factor ; health insurance, usual source for medical care of enabling factor were the siginificant exploratory variables for the health utilization. Out of the logistic regression analysis, health status, chronic illness, residence, marital status, education, drinking, use of health aid were found to be significant exploratory variables. From LISREL, need factor affect utilization most following by predisposing factor, enabling factor and health behavior. For LISREL model, age, education, and residence for predisposing factor; health status, chronic illess, and perceived health status for need factor; medical insurance for enabling factor; and doing any kind of health behavior for the health behavior were found as the significant observed variables for each theoretical variables.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.302-305
• /
• 2004

The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.

• Clinics in Shoulder and Elbow
• /
• v.16 no.1
• /
• pp.63-72
• /
• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• The Journal of Korean Academy of Prosthodontics
• /
• v.46 no.6
• /
• pp.610-619
• /
• 2008

STATEMENT OF PROBLEM: Factors affecting patients' decision-making for dental prosthetic treatment should be examined in terms of understanding improving patients' oral health. PURPOSE: The main purpose of this dissertation was to investigate patients' dental prosthetic treatment and factors affecting patients' decision-making for dental prosthesis treatment in Deagu and Gyungbook areas. MATERIAL AND METHODS: This study was based on the preliminary survey of dental patients conducted from July 1 to August 31 in 2006. A total of 700 questionnaires had been distributed and 640 were collected. 629 questionnaires were used for the statistical analysis. Descriptive and inferential statistics, such as frequencies, cross tabulation analysis, correlation analysis, logistic regression analysis, and multiple regression analysis were introduced. In the multiple regression analysis and logistic regression analysis, twenty-two independent variables were employed to explore the factors which have impacts on decision-making and satisfaction. RESULTS: The results of this dissertation are as follows: Logistic regression analysis turned out that monthly income, age, degree of expectation, marital status, and employer-insured policy of national insurance statistically increased the odds of decision-making of dental prosthesis treatment. But educational attainment decreased the odds ratio of the decision-making of dental prosthesis treatment. However, the rest independent variables do not have statistically significant impacts on the decision-making of dental prosthesis treatment CONCLUSION: Among independent variables, marital status had the most significant influence on the decision making of dental prosthesis treatment. Finally, suggestions for the future study and policy implications to improve satisfaction of the patients' dental prosthetic treatment were discussed.

• Journal of Korean Academy of Nursing
• /
• v.43 no.2
• /
• pp.154-164
• /
• 2013

Purpose: The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature. Methods: Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models. Results: Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis. Conclusion: Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.

• Journal of Information Technology Services
• /
• v.23 no.2
• /
• pp.1-12
• /
• 2024

This study aims to find ways to increase the declining donation intention by using machine learning techniques. To this end, in order to predict factors that affect donations between the MZ generation and the senior generation, various machine learning algorithms, including logistic regression analysis, are applied to build a model to determine variables that affect donation intention, and provide statistical verification and evaluation indicators. In this study, differences in donation intention by generation were expected as a variable affecting donation intention, and the senior generation was expected to show a higher donation intention tendency than the younger generation. However, although the research results were not statistically significant, the younger generation showed a higher intention to donate, and these results are interpreted to mean that value consumption and ethical consumption, which are important to today's MZ generation, also influenced donations. However, there were differences between generations in the amount of donations, and higher donation amounts were confirmed among the senior generation (those in their 50s or older) than the younger generation. In addition, the results of the logistic regression analysis showed that previous donation experience had a positive effect on future donation intention, and the more motivation and importance of donation and various social participation activities online and offline, the more active one became in donating.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.3086-3101
• /
• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• The Korean Journal of Emergency Medical Services
• /
• v.17 no.3
• /
• pp.169-179
• /
• 2013

Purpose: The performance rate to perform Cardiopulmonary Resuscitation (CPR) by witness in out-of-hospital Cardiac Arrest (OHCA) is very low in South Korea. To prevent the death caused by OHCA, it is important to encourage the witness to perform CPR actively. The purpose of the study is to investigate the influencing factors to affect bystander CPR rate. Methods: I conducted a questionnaire survey from 25 February to 4 March, 2013, receiving responses from 517 people in Korea. The questionnaire included social demographic factors, history of heart disease, knowledge of CPR, and the reliability of emergency medical service (EMS). A logistic regression analysis was conducted. Results: Among the 517 respondents, 294 (57.4%) had intention of performing CPR. Multiple logistic regression analysis found the following significant predictors of CPR intention: gender (odds ratio [OR] = 0.390), age (OR = 1.024), religion (OR = 0.843), and knowledge of CPR (OR = 4.734). Conclusion: This study indicated that the strongest predictor is knowledge of CPR. Therefore, it would be helpful to teach CPR nationwide to encourage performing CPR. In addition, effect of CPR education in religious facilities is necessary.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.4
• /
• pp.755-768
• /
• 2017

As Big Data becomes the core of the fourth industrial revolution, big data-based processing and analysis capabilities are expected to influence the company's future competitiveness. Comparative studies of RHadoop and RHIPE that integrate R and Hadoop environment, have not been discussed by many researchers although RHadoop and RHIPE have been discussed separately. In this paper, we constructed big data platforms such as RHadoop and RHIPE applicable to large scale data and implemented the machine learning algorithms such as multiple regression and logistic regression based on MapReduce framework. We conducted a study on performance and scalability with those implementations for various sample sizes of actual data and simulated data. The experiments demonstrated that our RHadoop and RHIPE can scale well and efficiently process large data sets on commodity hardware. We showed RHIPE is faster than RHadoop in almost all the data generally.