• Korean Journal of Radiology
• /
• v.23 no.1
• /
• pp.139-149
• /
• 2022

Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
• /
• 2007.11a
• /
• pp.169-174
• /
• 2007

• Journal of the Korean Orthopaedic Association
• /
• v.55 no.2
• /
• pp.143-153
• /
• 2020

Purpose: To evaluate the timing of the improvement in surgical skills of the direct anterior approach for hip arthroplasty through an analysis of the clinical features and learning curve in 58 cases. Materials and Methods: From November 2016 to November 2018, 58 patients, who were divided into an early half and late half, and underwent hip arthroplasty by the direct anterior approach, were enrolled in this retrospective study. The operation time and complications (fracture, lateral femoral cutaneous nerve injury, heterotopic ossification, infection, and dislocation) were assessed using a chi-square test, paired t-test, and cumulative sum (CUSUM) test. Results: The mean operation times in total hip arthroplasty (26 cases) and bipolar hemi-arthroplasty were 132.1 minutes and 79.7 minutes, respectively, demonstrating a significant difference between the two groups. CUSUM analysis based on the results revealed breakthrough points of the operation time, decreasing to less than the mean operation time because of the 16th case in total hip arthroplasty and 14th case in bipolar hemiarthroplasty. Complications were encountered in the early phase and late phase: five cases of fractures in the early phase, no case in the late phase; eight and two cases of lateral femoral cutaneous nerve injury, respectively; three and two cases of heterotopic ossification, respectively; and one case of dislocation, one case of infection and three cases of others in the early phase. The CUSUM chart for the fracture rate during operation in the early phase revealed the following: five cases fracture (17.2%) in the early phase and no case in the late phase (0%). This highlights the learning curve and the need for monitoring the inadequacy of operation based on the complications. Conclusion: Hip arthroplasty performed by the direct anterior approach based on an anatomical understanding makes it difficult to observe the surgical field and requires a learning curve of at least 30 cases.

• Steel and Composite Structures
• /
• v.37 no.2
• /
• pp.193-209
• /
• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.290-297
• /
• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.297-299
• /
• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.21 no.2
• /
• pp.215-226
• /
• 2015

Purpose: This study aimed to develop a self-assessment tool to evaluate program outcomes of nursing students in Korean nursing undergraduate education. Methods: The instrument development process consisted of literature review, focus group interviews, and item validation. A total of 117 items were analyzed through content validity testing. Data was gathered from 302 nursing students in Korea and analyzed using SPSS 21.0. Results: To construct validity, principal component analysis and Varimax rotation were used, and 12 factors, with a cumulative explanatory variance of 69.16%, were determined from 79 items. For internal consistency and reliability, Cronbach's ${\alpha}$ was .91. The half-split reliability results were .84 and .85, and the ROC curve showed an optimal cutpoint at 227. A five-point Likert scale was used for scoring. Conclusion: This instrument was found to have fair validity and reliability as a self-assessment tool for nursing student learning outcomes. Therefore, it can be used to evaluate program outcomes indirectly in nursing schools.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.354-354
• /
• 2022

딥러닝(Deep Learning)은 일반적으로 인공신경망(Artificial Neural Network) 를 의미하는데, 이에 따른 결과는 데이터의 양, 변수, 학습모델의 학습횟수, 은닉층(Hidden Layer)의 개수 등 여러 요소로 인해 결정된다. 본 연구에서는 물리적 장기유출 모형인 SWAT의 결과를 참값으로 LSTM모형의 매개변수인 은닉층 갯수와 학습횟수등의 시나리오를 바탕으로 검보정을 수행하였으며, 최적의 목적함수를 갖는 매개변수를 도출하였다. 이를 이용하여 유량지속곡선을 도출한결과를 SWAT의 결과와 비교해본 결과 매우 높은 상관성을 도출하였으며 이를 통해 수자원분야에서 인공신경망의 활용 가능성을 확인하였다.

• Journal of Biomedical Engineering Research
• /
• v.28 no.3
• /
• pp.355-362
• /
• 2007

Diabetes mellitus can cause devastating complications, which often result in disability and death, and diabetic nephropathy is a leading cause of death in people with diabetes. In this study, we tried to predict the onset of diabetic nephropathy from an irregular and unbalanced diabetic dataset. We collected clinical data from 292 patients with type 2 diabetes and performed preprocessing to extract 184 features to resolve the irregularity of the dataset. We compared several feature selection methods, such as ReliefF and sensitivity analysis, to remove redundant features and improve the classification performance. We also compared learning methods with support vector machine, such as equal cost learning and cost-sensitive learning to tackle the unbalanced problem in the dataset. The best classifier with the 39 selected features gave 0.969 of the area under the curve by receiver operation characteristics analysis, which represents that our method can predict diabetic nephropathy with high generalization performance from an irregular and unbalanced dataset, and physicians can benefit from it for predicting diabetic nephropathy.

• Journal of The Korean Association For Science Education
• /
• v.35 no.3
• /
• pp.465-475
• /
• 2015

The purpose of this study is to analyze the results of physics (optics) in nationwide standardized assessment and to investigate middle-school students' characteristics of achievement by using an option response rate distribution curve. For this purpose, we analyzed the 10 optics problems from the National Assessment of Educational Achievement (NAEA) items for middle school science subject conducted in 2010-2013. The results of this study are as follows; First, students showed a little higher achievement in optics than classical mechanics and electromagnetism. Second, students achieved significantly worse in 'formation of image' in 'light' part and 'variation of phase in propagation of wave' in 'wave' part. Third, students showed a context-dependent problem solving strategy and result. Additionally, we suggested some implications about the readjustment of some optics concepts level of national science curriculum, the need for teaching and learning strategies for basic level students, and the need for teaching and learning strategies focused on the realistic context.