• Journal of Korean Academy of Nursing
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• v.43 no.3
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• pp.442-451
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• 2013

Purpose: This descriptive study was done to develop a predictive model of depression in rural elders that will guide prevention and reduction of depression in elders. Methods: A cross-sectional descriptive survey was done using face-to-face private interviews. Participants included in the final analysis were 461 elders (aged${\geq}$ 65 years). The questions were on depression, personal and environmental factors, body functions and structures, activity and participation. Decision tree analysis using the SPSS Modeler 14.1 program was applied to build an optimum and significant predictive model to predict depression in rural elders. Results: From the data analysis, the predictive model for factors related to depression in rural elders presented with 4 pathways. Predictive factors included exercise capacity, self-esteem, farming, social activity, cognitive function, and gender. The accuracy of the model was 83.7%, error rate 16.3%, sensitivity 63.3%, and specificity 93.6%. Conclusion: The results of this study can be used as a theoretical basis for developing a systematic knowledge system for nursing and for developing a protocol that prevents depression in elders living in rural areas, thereby contributing to advanced depression prevention for elders.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2503-2508
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• 2005

In this paper, we propose a new discrete-time predictive current controller for a PMSM(Permanent Magnet Synchronous Motor). The main objectives of the current controllers are to ensure that the measured stator currents tract the command values accurately and to shorten the transient interval as much as possible, in order to obtain high-performance of ac drive system. The conventional predictive current controller is hard to implement in full digital current controller since a finite calculation time causes a delay between the current sensing time and the time that it takes to apply the voltage to motor. A new control strategy in this paper is seen the scheme that gets the fast adaptation of transient current change, the fast transient response tracking. Moreover, the validity of the proposed method is demonstrated by numerical simulations and the simulation results will present the improvements of predictive controller and accuracy of the current controller.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.657-660
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• 2015

Background: A diagnosis of H. pylori infection can be made by invasive or non-invasive methods. Several noninvasive diagnostic tests based on the detection of H. pylori stool antigen (HpSA) have been developed. The Genx H. pylori stool antigen card test is a new rapid, non-invasive test that is based on monoclonal immunochromatographic assay. The aim of this study was to determine its sensitivity, specificity, and diagnostic accuracy for diagnosing H. pylori infection in adult patients. Materials and Methods: A total of 162 patients were included in the study. A gastric biopsy was collected for histopathology and rapid urease testing. Stool specimens for HpSA testing were also collected. Patients were considered H. pylori positive if two invasive tests (histological and rapid urease tests) were positive. Results: Using the reference test, 50.6% of the samples were positive for H. pylori infection. The Genx H. pylori antigen test was positive in 19.7% of patients. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of the Genx H. pylori antigen test were 51.6%, 96.0%, 88.8%, 76.1%, and 79.0%, respectively. Conclusions: The Genx H. pylori stool antigen card test is a new non-invasive method that is fast and simple to perform but provides less reliable results.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.3
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• pp.458-471
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• 2013

This paper addresses the limitation of a map comparison metric entitled Figure of Merit through employing a simple land change model. The metric was originally designed to overcome limitations of other existing statistics, such as Kappa, when assessing predictive accuracy of land change models. A series of comparisons between null and predicted outcomes at multiple resolutions as well as a multi-resolution Figure of Merit analysis techniques of validation are compared for spatially segregated calibration and validation datasets. The Figure of Merit at the null resolution in this paper was 57%, although future research must be done to determine if this was simply a coincidence. A Figure of Merit greater than 50% would seem to represent a "Resolution of Merit" in that the Figure of Merit at that resolution becomes greater than the error. Thus, these two metrics should be used in tandem to assess predictive accuracy of a land change model.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.73-79
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• 2009

For the shake of control for movement object, control theory like neural network, nonlinear model predictive control(NMPC) is realized on digital high speed computer. Predictor of flight control system(FCS) based nonlinear model predictive control has to be satisfied with response for hard real-time to perform applications on each module in the FCS. Simultaneously, It gives a serious consideration accuracy to give full play to FCS's performance. Error of mathematical aspect affects realization of whole algorithm. But factors of bring mathematical error is not considered to calculate final accuracy on parameter of predictor. In this paper, Predictor was made using load control model on the digital computer for design FCS at hard real-time and is shown response time on realization algorithm. And is shown realization algorithm of high effective predictor over the accuracy. The predictor was realized on the load control model using Euler method, Heun method, Runge-Kutta and Taylor method.

• Korean Journal of Clinical Laboratory Science
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• v.39 no.3
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• pp.271-276
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• 2007

Helicobacter pylori (H. pylori) infection is common in korea and high incidence at gastric ulcer and duodenal ulcer. $^{14}C-urea$ breath test ($^{14}C-UBT$) is regarded as a highly reliable and non-invasive method for the diagnosis of H. pylori infection. The purpose of this study was to evaluate the diagnositc performance of a new and rapid $^{14}C-UBT$, which was equipped with Geiger-Muller counter and compared the results with those obtained by gastroduodenoscopic biopsies (GBx). One hundred sixty-eight patients (M : F = 118 : 50) underwent $^{14}C-UBT$, rapid urease test (CLO test), and GBx. The results of $^{14}C-UBT$ were classified as positive (>50 cpm), borderline (25$^{14}C-UBT$ or CLO test results with GBx as a glod standard. In the assessment of the presence of H. pylori infection, the $^{14}C-UBT$ global performance yielded positive predictive value, negative predictive value and accuracy of 93.3% and 83.3%, respectively. However, the CLO test had performance yielded positive predictive value, negative predictive value and accuracy of 76.9%, 50.0%, respectively. In this study $^{14}C-UBT$ is a highly accurate, simple and non-invasive method or the diagnosis of follow up H. pylori infection.

• Korean Journal of Head & Neck Oncology
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• v.8 no.1
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• pp.31-36
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• 1992

Neck mass is common neoplasms, but it poses a diagnostic dilemma for the physician. The differential diagnosis include neoplastic, inflammatory and developmental causes. The FNAC is one of the most valuable tests in the initial assessment and differential diagnosis of the neck mass. FNAC was performed with 267 cases of the neck mass, during the period from April, 1988 to October, 1990 at the department of General Surgery, Soon Chun Hyang. University Hospital. Thyroid lesions were excluded from this analysis. Final diagnosis was based on resection histology in 58 cases, and surgical specimens were compared with FNAC. The following results were obtoired ; 1) Of 267 cases, there we re 9 cases(3.4%) of congenital lesion, 74 cases(27.7%) of inflammatory lesion, 40 cases(15.0%) of benign tumor, 12 cases(4.5%) of primary malignant tumor, 37 cases(13.8%) of metastatic tumor, 75cases(28.1%) of reactive hyperplasia, 20 cases(7.5%) of unsatisfactory. In the pathologic classification, inflammatory lesion was the most common. 2) In the 58 cases of excisional biopsy, sensitivity 93.8%, specificity 95.2%, false positive 11.8%, false negative 2.4%, positive predictive value 88.2%, negative predictive value 97.6%, accuracy 94.8%. 3) The most common disease was the tuberculous lymphadenitis (53 cases, 19.8%). sensitivity 57.9%, specificity 100.0%, false positive 0.0%, false negative 17.0%, positive predictive value 100.0%, negative predictive value 83.0%, accuracy 86.2%.