• 제어로봇시스템학회논문지
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• 제8권2호
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of Periodontal and Implant Science
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• 제46권1호
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• pp.10-21
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• 2016

Purpose: This study evaluated the predictive performance of a combination of self-report questionnaires, salivary hemoglobin levels, and age as a non-invasive screening method for periodontitis. Methods: The periodontitis status of 202 adults was examined using salivary hemoglobin levels, responses to 10 questions on a self-report questionnaire, and the Community Periodontal Index (CPI). The ability of those two variables and the combination thereof with age to predict the presence of CPI scores of 3-4 and 4 was assessed using logistic regression and receiver operating characteristic (ROC) curve analysis. Results: CPI scores of 3-4 and 4 were present among 79.7% and 46.5% of the sample, respectively. The area under the ROC curves (AUROCs) of salivary hemoglobin levels for predicting prevalence of CPI scores of 3-4 and 4 were 0.63 and 0.67, respectively (with sensitivity values of 71% and 60% and specificity values of 56% and 72%, respectively). Two distinct sets of five questions were associated with CPI scores of 3-4 and 4, with AUROCs of 0.73 and 0.71, sensitivity values of 76% and 66%, and specificity values of 63% and 69%. The combined model incorporating both variables and age showed the best predictive performance, with AUROCs of 0.78 and 0.76, sensitivity values of 71% and 65%, and specificity values of 68% and 77% for CPI scores of 3-4 and 4, respectively. Conclusions: The combination of salivary hemoglobin levels and self-report questionnaires was shown to be a valuable screening method for detecting periodontitis.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 대한간호학회지
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• 제50권6호
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• pp.799-812
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• 2020

Purpose: This study aimed to construct and test a predictive model that explains and predicts the quality of life in older men living alone. Methods: A self-report questionnaire was used to collect data from 334 older adult men living along aged 65 years or over living in Jeollanam-do provinces. The endogenous variables were depression, self-rated health, instrumental activity of daily life, health promotion behaviors, the number of social participation activities and quality of life. Data were analyzed using the SPSS 21.0 and AMOS 21.0 programs. Results: The final model with 14 of the 8 analysed paths showed a good fit to the empirical data: χ2 = 173.26(p < .001, df = 53), normed χ2 = 3.27, GFI = .92, NFI = .90, CFI = .93, TLI = .89, RMSEA = .08 and SRMR = .06. Activities had direct effect on quality of life of older men living alone and social support had both direct and indirect effects. Meanwhile, function and socioeconomic status showed only indirect effects. The variables included in the eight significant paths explained 83.7% of variance in the prediction model. Conclusion: Instrumental activities of daily living and social support effect directly on quality of life in the older men living alone. Findings suggest that health care providers including community nurses need to provide social support as well as empowerment programs of instrumental activities of daily living and health promotion for improving quality of life of the older men living alone.

• 정보교육학회논문지
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• 제24권6호
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• pp.597-606
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• 2020

최근 전세계가 언택트 환경에 놓임에 따라 학생들의 프로그래밍 수업도 온라인으로 이루어지게 되었고, 온라인 프로그래밍 수업을 성공으로 이끌 수 있는 영향요인들에 대한 관심이 커지고 있다. 이에 본 연구에서는 특성화 고등학교 학생들을 대상으로 웹기반 시뮬레이션 툴을 활용하여 온라인 프로그래밍 수업을 진행하였다. 그리고 온라인 프로그래밍 수업에서 학생들의 학습참여와 교수실재감에 영향을 주는 변인으로 자기조절능력과 학습 몰입을 상정하고 예측력을 분석하였다. 또한 학습참여, 교수실재감과 학습자의 자기조절능력 사이에서 학습몰입의 매개효과를 분석하였다. 연구 결과 온라인 프로그래밍 수업에서 자기조절능력과 학습몰입이 학습참여와 교수 실재감을 예측하는 것으로 나타났고, 학습몰입은 자기조절능력과 학습참여, 교수실재감 사이에서 매개역할을 하는 것으로 나타났다. 본 연구는 온라인 프로그래밍 수업에서 학습참여와 교수실재감을 높이기 위해 자기조절능력과 학습몰입이 고려되어야 함을 제안하고, 이를 위한 실천적 시사점을 제공하였다는 데 의의가 있다.

• 대한간호학회지
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• 제52권3호
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• pp.308-323
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• 2022

Purpose: This study aimed to construct and test a predictive model for the quality of life (QOL) in mothers of children with developmental disabilities (DB). The hypothesized model included severity of illness, distress, uncertainty, self-help, and parenting efficacy as influencing factors, QOL as a consequence based on the Braden's Self-Help Model. Methods: The data were collected through a direct and online surveys from 206 mothers in 8 locations, including welfare or daycare centers, developmental treatment centers, and The Parents' Coalition for the Disabled located in two provinces of Korea. Data were analysed using SPSS/WIN 23.0 and AMOS 21.0 program. Results: The fit indices of the predictive model satisfied recommended levels; 𝛘² = 165.79 (p < .001), normed 𝛘² (𝛘²/df) = 2.44, RMR = .04, RMSEA = .08, GFI = .90, AGFI = .85, NFI = .91, TLI = .93, CFI = .95. Among the variables, distress (β = - .46, p < .001), parenting efficacy (β = .22, p < .001), and self-help (β = .17, p = .018) had direct effects on QOL. Severity of illness (β = - .61, p = .010) and uncertainty (β = - .08, p = .014) showed indirect effects. The explanatory power of variables was 61.0%. Conclusion: The study results confirm the utility of Braden's Self-Help Model. They provide a theoretical basis for improving QOL in mothers of children with DB. Nursing intervention strategies that can relieve mothers' distress and uncertainty related to disease and enhance parenting efficacy and self-help behavior should be considered.

• 지식경영연구
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• 제24권4호
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• pp.63-85
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• 2023

본 연구는 코로나-19 시대를 거치며 증가한 메타버스 공간에 관한 관심을 바탕으로 주된 이용층을 확인하고 이를 예측하는 변인을 탐색하고자 했다. 온라인 활동을 예측하기 위해서는 이용자 이용 목적과 동기 및 관련된 인구통계적 요인을 확인해야 하므로 이를 예측 변인으로 모형 분석을 진행했다. 2022년 한국미디어패널조사 데이터를 바탕으로 메타버스 이용자를 예측하는 Heckman 2단계 표본선택모형 분석을 수행했다. 분석 결과 1단계 선택모형에서 메타버스 이용을 결정하는 주된 요인으로는 오프라인 활동, 개방성, OTT 이용 여부, 그리고 유료 콘텐츠 구입 여부가 확인되었다. 또한 2단계 결과모형에서는 개방성, 성별, 유료 콘텐츠 구입 여부가 메타버스 이용 시간을 높이는 주된 변인으로 확인되었다. 이 연구 결과는 코로나-19 시대 온라인 활동 증가와 함께 메타버스 서비스에 관한 관심이 높아지고 있는 상황에서, 메타버스 이용자를 이해하고 예측하는 데 기여할 수 있을 것이다. 또한 메타버스 플랫폼 관련 기업과 개발자에게 유용한 정보를 제공할 수 있을 것이다.

• 대한간호학회지
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• 제54권2호
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• pp.193-210
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• 2024

Purpose: The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." Methods: The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix. Results: The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol. Conclusion: Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care.

• Journal of Trauma and Injury
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• 제37권2호
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• pp.124-131
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• 2024

Purpose: The aim of this study was to utilize the American College of Surgeons Trauma Quality Improvement Program (TQIP) database to identify risk factors associated with developing acute compartment syndrome (ACS) following lower extremity fractures. Specifically, a nomogram of variables was constructed in order to propose a risk calculator for ACS following lower extremity trauma. Methods: A large retrospective case-control study was conducted using the TQIP database to identify risk factors associated with developing ACS following lower extremity fractures. Multivariable regression was used to identify significant risk factors and subsequently, these variables were implemented in a nomogram to develop a predictive model for developing ACS. Results: Novel risk factors identified include venous thromboembolism prophylaxis type particularly unfractionated heparin (odds ratio [OR], 2.67; 95% confidence interval [CI], 2.33-3.05; P<0.001), blood product transfusions (blood per unit: OR 1.13 [95% CI, 1.09-1.18], P<0.001; platelets per unit: OR 1.16 [95% CI, 1.09-1.24], P<0.001; cryoprecipitate per unit: OR 1.13 [95% CI, 1.04-1.22], P=0.003). Conclusions: This study provides evidence to believe that heparin use and blood product transfusions may be additional risk factors to evaluate when considering methods of risk stratification of lower extremity ACS. We propose a risk calculator using previously elucidated risk factors, as well as the risk factors demonstrated in this study. Our nomogram-based risk calculator is a tool that will aid in screening for high-risk patients for ACS and help in clinical decision-making.

• Tuberculosis and Respiratory Diseases
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• 제58권3호
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• pp.230-242
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• 2005

연구배경 : 우리나라 국민을 대상으로 실시한 폐활량검사의 판정과 해석은 우리나라 국민을 대상으로 구한 폐활량 예측식이 사용되어야 한다. 그 동안 외국인을 대상으로 구한 폐활량 예측치가 사용되어 오류가 있었다. 본 연구에서는 전 국민을 대상으로 대표성 있고 신뢰할 수 있는 노력성 폐활량과 일초간 노력성 폐활량, 6초간 노력성 폐활량, 일초율에 대한 정상 예측식을 개발 하고자 하였다. 연구방법 : 전 국민을 대상으로 층화표본 추출법을 사용하여 조사대상을 선정하였으며, 폐활량검사기와 검사방법, 검사과정, 결과의 선택을 미국흉부학회에서 권고하는 기준에 따라 체계적인 정도관리와 질관리를 실시하였다. 폐활량 검사를 실시한 대상자 4,816명 중에서 비흡연자이면서 폐활량에 영향을 미칠 수 있는 호흡기 질환 및 증상이 없고, 흉부방사선학적 검사에서 심폐 이상 소견이 없으면서, 폐활량에 영향을 미치는 유해인자에의 노출력이 없는 대상자는 1,212명으로 남자 206명, 여자 1,006명이었다. 이들은 지역과 연령별로 우리나라 국민을 대표할 수 있었다. 통계분석에서 혼합효과모델을 적용하여 AIC 값이 가장 작은 모델로서 남자와 여자에 공통적으로 포함된 변수들을 일반선형회귀분석에 적용하여 폐활량 예측식을 구하였다. 결 과 : 노력성 폐활량의 예측식은 남자 -4.8434 - 0.00008633*연령$^2$(년) + 0.05292*신장(cm) + 0.01095*체중(kg)이었으며, 여자 -3.0006 - 0.0001273 *연령$^2$(년) + 0.03951*신장(cm) + 0.006892*체중(kg)이었다. 일초간 노력성 폐활량의 예측식은 남자 -3.4132 -0.0002484*연령$^2$(년) + 0.04578*신장(cm)이었으며, 여자 -2.4114 - 0.0001920*연령$^2$(년) + 0.03558*신장(cm)이었다. 6초간 노력성 폐활량의 예측식은 남자 -4.4244 -0.0001367*연령$^2$(년) + 0.05156*신장(cm) + 0.008246*체중(kg)이었으며, 여자 -3.1433 - 0.0001442*연령$^2$(년) + 0.04018*신장(cm) + 0.007077*체중(kg)이었다. 일초율의 예측식은 남자 119.9004 - 0.3902*연령(년) - 0.1268*신장(cm)이었으며, 여자 97.8567 - 0.2800*연령(년) - 0.01564*신장(cm)이었다. 결 론 : 본 예측식은 미국흉부학회에서 제시하고 있는 연령과 신장 변수에 체중이 포함되어 차이가 있었다. 이러한 이유는 연령효과와 젊은 연령에서 신장과 체중이 급격하게 변화하는 체격효과가 복합적으로 작용하기 때문이라고 해석된다. 본 예측식과 다른 국내 및 국외 예측식을 비교할 때 본 예측식이 노력성 폐활량과 일초간 노력성 폐활량, 일초율의 예측치를 높게 추정하였으나 대부분 그 차이가 10% 이내로 비슷하였다. 코카시안인 백인을 대상으로 구한 외국의 예측식보다 본 연구의 정상 예측치가 낮지 않았다. 이러한 이유로는 우리나라 젊은 사람들의 체격조건의 변화와 함께 엄격한 정상인의 선정기준, 검사방법과 검사결과의 정도관리 및 질관리에 기인하는 것으로 판단되었다.