• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.47-47
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• 2023

Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R²=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Asian Pacific Journal of Cancer Prevention
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• 제14권10호
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• pp.6019-6023
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• 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

• Tuberculosis and Respiratory Diseases
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• 제86권3호
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• 대한지리학회지
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• 제49권6호
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• pp.917-934
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• 2014

지리가중회귀 모델(GWR)은 국지적으로 이질적인 부동산 가격을 추정할 수 있는 도구로 폭넓게 활용되어 왔다. 그럼에도 불구하고 GWR은 공간적으로 이질적인 가격결정요인의 선택이나 국지적 추정에서의 관측치 수의 제한 등과 같은 한계를 가지고 있다. 본 연구는 이러한 한계를 극복하기 위한 대안으로 최근 주목받고 있는 지리가중라소 모델(GWL)을 이용하여 국지적으로 다양한 부동산 가격결정요인들을 탐색하고, 부동산 가격 추정에 있어서 GWL 모델의 적용가능성을 살펴보고자 한다. 이를 위해 서울시 아파트 가격을 대상으로 OLS, GWR, GWL의 헤도닉 모델을 구축하였으며, 모델의 설명력, 예측력, 다중공선성 측면에서 이들을 비교 분석하였다. 그 결과, 전역적 모델에 비해 국지적 모델이 전체적인 설명력, 예측력이 우수한 것으로 나타났으며, 특히 국지적 모델 중 GWL 모델은 다중공선성 문제를 자동적으로 해결하면서 공간적으로 이질적인 가격 결정요인 집합들을 도출하였고, 다른 모델들에 비해 상당히 높은 설명력과 예측력을 보여주고 있다. 본 연구에서 적용한 GWL 모델은 고차원의 데이터셋에서 유의미한 독립 변수들을 효율적으로 선정하는데 직접적인 도움을 줌으로써 부동산과 같이 대용량의 복잡한 구조를 가진 공간 빅데이터를 위한 유용한 분석 기법으로 활용될 수 있을 것이다.

• Korean Journal of Radiology
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• 제23권8호
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

• 한국도로학회논문집
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• 제7권4호
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• pp.69-77
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• 2005

줄눈 콘크리트포장에서 적정한 줄눈간격은 슬래브의 거동에 의해 발생하는 인장응력 줄눈채움재의 파손과 다웰바의 하중전달율(LTE)의 저하를 방지하여 도로포장의 장기 공용성 증가에 큰 영향을 미친다. 하지만 국내 줄눈간격은 경험적이고 획일적으로 이루어진다. 그러므로 본 연구는 국내 권역별 기후조건을 토대로 합리적인 줄눈간격 산정에 대한 방안을 제시하였다. 합리적인 줄눈간격은 초기거동에서 환경하중의 영향에 따른 건조수축과 수화열에 의해 무작위 균열 발생하지 않도록 산정하였다. 그리고 장기거동에서 줄눈폭의 과도한 움직임에 따른 LTE 저하로 인해 포장파손이 발생하지 않도록 줄눈간격을 산정하였다. 본 연구에서 산정된 줄눈간격 잠정안은 6$\sim$8m이며, 포장파손 예측 모델을 통해 줄눈간격을 8m로 증가시켜도 포장 공용성엔 큰 차이가 없었다.