• 응용통계연구
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• 제1권2호
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• pp.45-60
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• 1987

癌 환자에서 癌의 骨髓轉移 여부를 판정하는 것은 임상적으로 매우 중요하다. 癌의 骨髓轉移에 대한 說明變數 을 찾기 위하여 癌의 骨髓轉移가 있는 ？ 60 例와 公水轉移가 없는 환자 41 例를 대상으로 로지스틱 回歸分析을 시도하였다. 上記 資料는 1977년 1월부터 1985년 12월까지 연세대학교 의과대학 부속 세브란스 병원의 기록을 後向的으로(retrospectively) 조사하여 수집되었다. 가장 적합도가 높은 로지스틱 回歸分析의 說明變數를 기초로 하여 임상적으로 적용이 편리한 癌의 骨髓轉移에 대한 判定基準을 構成하였고, 이러한 判定基準의 銳敏度(sensitivity)와 特異度(specificity)도 계산되었다.

• 품질경영학회지
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• 제46권3호
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• pp.453-464
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• 2018

Purpose: The purpose of this study is to propose a solution to the case where $V_{50}$ calculation is impossible in the process of bulletproof test. Methods: In this study, we proposed a $V_{50}$ estimation method using logistic regression analysis. Six scenarios were applied by combining the homogeneity of the sample and the speed range. Then, 1,000 simulations were performed per scenario and six assumptions reflecting the reality were applied. Results: The result of the study, it was confirmed that there was no statistical difference between the $V_{50}$ value calculated by the conventional method and the $V_{50}$ value calculated by the improvement method. Therefore, in situations where $V_{50}$ can not be calculated, it is reasonable to use logistic regression analysis. Conclusion: This study develops a methodology that is easy to use and reliable by using statistical model based on actual data.

• 한국지구과학회지
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• 제31권5호
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• pp.454-464
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• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.931-940
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• 2011

분류분석은 학습표본으로부터 분류규칙을 도출한 후 새로운 표본에 적용하여 특정 범주로 분류하는 방법이다. 데이터의 복잡성에 따라 다양한 분류분석 방법이 개발되어 왔지만, 데이터 차원이 높고 변수간 상관성이 높은 경우 정확하게 분류하는 것은 쉽지 않다. 본 연구에서는 데이터차원이 상대적으로 높고 변수간 상관성이 높을 때 강건한 분류방법을 제안하고자 한다. 부분최소자승법은 연속형데이터에 사용되는 기법으로서 고차원이면서 독립변수간 상관성이 높을 때 예측력이 높은 통계기법으로 알려져 있는 다변량 분석기법이다. 벌점 부분최소자승법을 이용한 분류방법을 실제데이터와 시뮬레이션을 적용하여 성능을 비교하고자 한다.

• 응용통계연구
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• 제32권5호
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• pp.653-669
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• 2019

뉴미디어의 등장과 발달로 인해, 과거 미디어의 대부분을 차지한 종이신문의 이용량이 점차 줄어들어 종이신문의 정기구독률이 2016년 14%로 매우 저조하게 나타났다. 따라서 본 연구는 종이신문 정기구독 여부에 영향을 미치는 열독자 요인을 파악하고자 수행되었다. 이를 위해 한국 언론 진흥재단의 2016년과 2017년의 언론수용자 의식조사의 자료를 분석에 사용하였다. 열독자의 성별, 연령, 학력, 가구소득, 열독일수, 열독시간, 열독분량을 열독자의 특성으로 지정하였으며, 정기구독 여부에 열독자의 어떠한 특성이 영향을 미치는지 알아보기 위해 다중 로지스틱 회귀를 적합하고 해석하였다.

• 한국시뮬레이션학회논문지
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• 제27권1호
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• pp.51-60
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• 2018

본 연구는 혁신확산이론과 관련 선행연구를 기반으로 사용자의 핀테크 결제 서비스 사용 영향요인을 서비스와 사용자 측면으로 분류하여 파악하고 인과관계를 실증 검증하였다. 사용자 수용에 영향을 미치는 서비스 특성으로 복잡성, 혜택, 서비스 제공자 신뢰와 인지된 위험을 서비스 특성으로, 개인 혁신성과 보안사고 경험을 사용자 특성으로 분류하여 연구모형을 구성하였다. 이항 로지스틱 회귀분석 결과 인지된 위험과 사용의 복잡성, 보안사고 경험은 사용자의 핀테크 수용에 부정적인 영향을 미치며 개인혁신성은 긍정적인 영향을 미치는 것으로 나타났다. 추가로 인지된 위험이 핀테크 서비스 수용에 미치는 부정적인 영향을 강화하는 보안사고 경험의 조절효과 역시 유의미하게 나타났다.

• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.603-624
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• 2020

There is an emerging interest in brain functional connectivity (FC) based on functional Magnetic Resonance Imaging in Alzheimer's disease (AD) studies. The complex and high-dimensional structure of FC makes it challenging to explore the association between altered connectivity and AD susceptibility. We develop a pipeline to refine FC as proper covariates in a penalized logistic regression model and classify normal and AD susceptible groups. Three different quantification methods are proposed for FC refinement. One of the methods is dimension reduction based on common component analysis (CCA), which is employed to address the limitations of the other methods. We applied the proposed pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and deduced pathogenic FC biomarkers associated with AD susceptibility. The refined FC biomarkers were related to brain regions for cognition, stimuli processing, and sensorimotor skills. We also demonstrated that a model using CCA performed better than others in terms of classification performance and goodness-of-fit.

• 한국인공지능학회지
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• 제9권1호
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.339-350
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• 2021

In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.

• Journal of Korean Biological Nursing Science
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• 제25권4호
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• pp.295-305
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• 2023

Purpose: This study compared health behaviors and health-related clinical characteristics between individuals with normal glucose levels without diabetes and those with undiagnosed diabetes. Factors that were associated with undiagnosed diabetes were identified by sex. Methods: This was an observational study with a cross-sectional design based on data from the eighth Korea National Health and Nutrition Examination Survey, which used a stratified, multi-stage, cluster-sampling design to obtain a nationally representative sample. Multiple logistic regression analysis was employed to compute the odds ratios of health behaviors and clinical characteristics to identify risk factors for undiagnosed diabetes. Results: The overall prevalence of undiagnosed diabetes was 5.2% (weighted %, n = 700, p < .001). Among individuals with undiagnosed diabetes, 58.3% were men. Univariate logistic regression for undiagnosed diabetes identified sex, age, house income, educational level, and triglycerides as influencing factors. In multiple logistic regression by sex, the factors associated with undiagnosed diabetes in men were age, perceived health status, a diagnosis of angina, and triglycerides. Conclusion: Strategies should be targeted to improve health behaviors and clinical characteristics for specific age groups, men in bad perceived health status, women with high systolic blood pressure, and high triglycerides. Moreover, healthcare providers should understand the barriers to health behaviors and health-related quality of life to effectively deliver healthcare services.