• Title, Summary, Keyword: logistic regression analysis

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On Logistic Regression Analysis Using Propensity Score Matching (성향점수매칭 방법을 사용한 로지스틱 회귀분석에 관한 연구)

  • Kim, So Youn;Baek, Jong Il
    • Journal of Applied Reliability
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    • v.16 no.4
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    • pp.323-330
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    • 2016
  • Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

Comparison between Logistic Regression and Artificial Neural Networks as MMPI Discriminator (MMPI 분석도구로서 인공신경망 분석과 로지스틱 회귀분석의 비교)

  • Lee, Jaewon;Jeong, Bum Seok;Kim, Mi Sug;Choi, Jee Wook;Ahn, Byung Un
    • Korean Journal of Biological Psychiatry
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    • v.12 no.2
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    • pp.165-172
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    • 2005
  • Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

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Study on Accident Prediction Models in Urban Railway Casualty Accidents Using Logistic Regression Analysis Model (로지스틱회귀분석 모델을 활용한 도시철도 사상사고 사고예측모형 개발에 대한 연구)

  • Jin, Soo-Bong;Lee, Jong-Woo
    • Journal of the Korean Society for Railway
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    • v.20 no.4
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    • pp.482-490
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    • 2017
  • This study is a railway accident investigation statistic study with the purpose of prediction and classification of accident severity. Linear regression models have some difficulties in classifying accident severity, but a logistic regression model can be used to overcome the weaknesses of linear regression models. The logistic regression model is applied to escalator (E/S) accidents in all stations on 5~8 lines of the Seoul Metro, using data mining techniques such as logistic regression analysis. The forecasting variables of E/S accidents in urban railway stations are considered, such as passenger age, drinking, overall situation, behavior, and handrail grip. In the overall accuracy analysis, the logistic regression accuracy is explained 76.7%. According to the results of this analysis, it has been confirmed that the accuracy and the level of significance of the logistic regression analysis make it a useful data mining technique to establish an accident severity prediction model for urban railway casualty accidents.

Comparing Risk-adjusted In-hospital Mortality for Craniotomies : Logistic Regression versus Multilevel Analysis (로지스틱 회귀분석과 다수준 분석을 이용한 Craniotomy 환자의 사망률 평가결과의 일치도 분석)

  • Kim, Sun-Hee;Lee, Kwang-Soo
    • The Korean Journal of Health Service Management
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    • v.9 no.2
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    • pp.81-88
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    • 2015
  • The purpose of this study was to compare the risk-adjusted in-hospital mortality for craniotomies between logistic regression and multilevel analysis. By using patient sample data from the Health Insurance Review & Assessment Service, in-patients with a craniotomy were selected as the survey target. The sample data were collected from a total number of 2,335 patients from 90 hospitals. The sample data were analyzed with SAS 9.3. From the results of the existing logistic regression analysis and multilevel analysis, the values from the multilevel analysis represented a better model than that of logistic regression. The intra-class correlation (ICC) was 18.0%. It was found that risk-adjusted in-hospital mortality for craniotomies may vary in every hospital. The agreement by kappa coefficient between the two methods was good for the risk-adjusted in-hospital mortality for craniotomies, but the factors influencing the outcome for that were different.

FACTORS AFFECTING PATIENTS' DECISION-MAKING FOR DENTAL PROSTHETIC TREATMENT

  • Jung, Hyo-Kyung;Kim, Han-Gon
    • The Journal of Korean Academy of Prosthodontics
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    • v.46 no.6
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    • pp.610-619
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    • 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.

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

  • Wu, Ya-Zhou;Yang, Huan;Zhang, Ling;Zhang, Yan-Qi;Liu, Ling;Yi, Dong;Cao, Jia
    • Asian Pacific Journal of Cancer Prevention
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    • v.13 no.5
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    • pp.2031-2037
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    • 2012
  • Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial human disease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes or environmental exposures. Based on our previous case-control study in Chongqing of China, we have found increased risk of colorectal cancer exists in individuals carrying a novel homozygous TT at locus rs1329149 and known homozygous AA at locus rs671. Methods: In this study, we proposed statistical method-crossover analysis in combination with logistic regression model, to further analyze our data and focus on assessing gene-environmental interactions for colorectal cancer. Results: The results of the crossover analysis showed that there are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactorial logistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicative interaction with alcohol consumption. Moreover, we also found additive interactions between any pair of two factors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through the crossover analysis, which was not evident on logistic regression. Conclusions: In conclusion, the method based on crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environment interactions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption in the pathogenesis and development of colorectal cancer.

Multicollinarity in Logistic Regression

  • Jong-Han lee;Myung-Hoe Huh
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.303-309
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    • 1995
  • Many measures to detect multicollinearity in linear regression have been proposed in statistics and numerical analysis literature. Among them, condition number and variance inflation factor(VIF) are most popular. In this study, we give new interpretations of condition number and VIF in linear regression, using geometry on the explanatory space. In the same line, we derive natural measures of condition number and VIF for logistic regression. These computer intensive measures can be easily extended to evaluate multicollinearity in generalized linear models.

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A Machine Learning based Ransomware Detection Model using a Hybrid Analysis (하이브리드 분석을 통한 머신러닝 기반의 랜섬웨어 탐지 모델)

  • Kim, Ji-Won;Ji, Seon-Hak;Kim, Sung-Ryul
    • Journal of Security Engineering
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    • v.14 no.4
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    • pp.263-280
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    • 2017
  • Ransomware, which recently being highlighted is evolving in some diverse aspects from random attacks targeting civilians continue to specific target (target attack) attack. Ransomware has developed into a business-based service RaaS (Ransomware as a Service) model and a new ransomware variant is also increasing. In this paper, we propose a detective model for ransomware through Logistic Regression Analysis technique using Opcode information, the API information as an independent variable, determine the ransomware as a dependent variable. These results show that the hybrid detection rate outperformed the static and dynamic detection rate, and the logistic regression technique outperformed the SVM, naïve Bayes technique.

Landslide Susceptibility Analysis and its Verification using Likelihood Ratio, Logistic Regression and Artificial Neural Network Methods: Case study of Yongin, Korea

  • Lee, S.;Ryu, J. H.
    • Proceedings of the KSRS Conference
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    • pp.132-134
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    • 2003
  • The likelihood ratio, logistic regression and artificial neural networks methods are applied and verified for analysis of landslide susceptibility in Yongin, Korea using GIS. From a spatial database containing such data as landslide location, topography, soil, forest, geology and land use, the 14 landsliderelated factors were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by likelihood ratio, logistic regression and artificial neural network methods. Before the calculation, the study area was divided into two sides (west and east) of equal area, for verification of the methods. Thus, the west side was used to assess the landslide susceptibility, and the east side was used to verify the derived susceptibility. The results of the landslide susceptibility analysis were verified using success and prediction rates. The v erification results showed satisfactory agreement between the susceptibility map and the exis ting data on landslide locations.

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Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

  • Lee, Junghoon;Jung, Ho-Yeon;Koo, J.R.;Yoon, Yoonjin;Jung, Hyung-Jo
    • Journal of Electrical Engineering and Technology
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    • v.12 no.2
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    • pp.969-980
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    • 2017
  • Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.