• Archives of Pharmacal Research
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• v.19 no.5
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• pp.386-389
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• 1996

The in vivo behaviour of phenylephrine hydrochloride in different vehicles like gels of Carbopol $907^circledR$, Carbopol $934P^circledR$ and latex system of cellulose acetate hydrogen phthalate(CAHP) was evaluated by measuring the reduction in intraocular pressure and the mydriatic activity. The parameters that haave been utilised to assess the performance of the formulations were the area under the curve (AUC), the maximum mydriasis $(I_{max})$, ethe time of maximum response $(T_{max})$ and the duration of activity (D). The influence of viscosity and mucoadhesion on the bioavailability parameters has also been investigated. Carbopol 934P and CAHP formulations showed prolonged duration of action and greater AUC compared to Carbopol 907 aqueous solution(P<0.05).

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.323-325
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• 2021

In this article, we have improved the prediction of hypertension detection using the feature selection method for the Korean national health data named by the KNHANES database. The study identified a variety of risk factors associated with chronic hypertension. The paper is divided into two modules. The first of these is a data pre-processing step that uses a factor analysis (FA) based feature selection method from the dataset. The next module applies a predictive analysis step to detect and predict hypertension risk prediction. In this study, we compare the mean standard error (MSE), F1-score, and area under the ROC curve (AUC) for each classification model. The test results show that the proposed FIFA-OE-NB algorithm has an MSE, F1-score, and AUC outcomes 0.259, 0.460, and 64.70%, respectively. These results demonstrate that the proposed FIFA-OE method outperforms other models for hypertension risk predictions.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.3-20
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• 2019

With advancements in technology of virtual reality, it is used for various purposes in many fields such as medical care and healthcare, but as the same time there are also increasing reports of nausea, eye fatigue, dizziness, and headache from users. These symptoms of motion sickness are referred to as cybersickness, and various researches are under way to solve the cybersickness problem because it can cause inconvenience to the user and cause adverse effects such as discomfort or stress. However, there is no official standard for the causes and solutions of cybersickness at present. This is also related to the absence of tools to quantitatively measure the cybersickness. In order to overcome these limitations, this study proposed quantitative and objective cybersickness evaluation method. We measured 128-channel EEG waves from ten participants experiencing visually stimulated virtual reality. We calculated the relative power of delta and alpha in 11 regions (left, middle, right frontal, parietal, occipital and left, right temporal lobe). Multiple regression models were obtained in a stepwise manner with the motion sickness susceptibility questionnaire (MSSQ) scores indicating the susceptibility of the subject to the motion sickness. A multiple regression model with the highest under the area ROC curve (AUC) was derived. In the multiple regression model derived from this study, it was possible to distinguish cybersickness by accuracy of 95.1% with 11 explanatory variables (P_D.MF, P_D.LP, P_D.MP, P_D.RP, P_D.MO, P_A.LF, P_A.MF, P_A.RF, P_A.LP, P_A.RP, P_A.MO). In summary, in this study, objective response to cybersickness was confirmed through 128 channels of EEG. The analysis results showed that there was a clearly distinguished reaction at a specific part of the brain. Using the results and analytical methods of this study, it is expected that it will be useful for the future studies related to the cybersickness.

• The Korean Journal of Pain
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• v.30 no.1
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• pp.51-58
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• 2017

Background: Recently symptoms-based screening questionnaires have gained attention for screening for a neuropathic pain component (NePC) in various chronic pain conditions. The present study assessed the usefulness of four commonly used NePC screening questionnaires including the Self-completed douleur neuropathique 4 (S-DN4), the ID Pain, the painDETECT questionnaire (PDQ), and the Self-completed Leeds Assessment of neuropathic Symptoms and Signs (S-LANSS) questionnaire in patients with chronic low back pain (CLBP) to assess the presence of NePC. Methods: This is a single-center cross-sectional study where patients with CLBP, with or without leg pain, were included. Participants were initially screened for NePC presence by a physician according to the regular practice, and later assessed using screening questionnaires. The diagnostic accuracy of these questionnaires was compared assuming the physician-made diagnosis as the gold standard. Results: A total of 215 patients with CLBP of which 164 (76.3%, 95% CI, 70.2-81.5) had a NePC were included. S-DN4, ID Pain, and PDQ have an area under the curve (AUC) > 0.8 indicating excellent discrimination. However, S-LANSS has an AUC of 0.69 (0.62-0.75), indicating low discrimination. S-DN4 has a significantly higher AUC as compared to ID Pain (d(AUC) = 0.063, P < 0.01) and S-LANSS (d(AUC) = 0.197, P < 0.01). But the AUC of S-DN4 does not significantly differ from that of PDQ (d(AUC) = 0.013, P = 0.62). Conclusions: S-DN4, ID Pain, and PDQ, but not S-LANSS, have good discriminant validity to screen for NePCs in patients with CLBP. Despite using all the tests, 20-30% of patients with an NePC were missed. Thus, these questionnaires can only be used as an initial clue in screening for NePCs, but do not replace clinical judgment.

• Korean Journal of Oriental Medicine
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• v.17 no.2
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• pp.107-113
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• 2011

Objectives : Sasang typology is extensively studied for the Sasang constitution diagnosis objectification with various data, for example, questionaires, reference materials, etc and analyzed with the several statistical methods. In this study, we used ROC-curve (Receiver Operating Characteristic curve) analysis to diagnose Sasang constitution, which is a kind of epidemiologic research methods and is away from traditional statistical methods. Methods : We collected personality questionnaire which consists of 15 items, from 24 oriental medical clinics. We analyzed the sensitivity and specificity using ROC curve method based on the score of personality questionnaire and also investigated classification accuracy and cut-off value of Sasang constitution. Results : The AUC (area under the ROC curve) value was 0.508 (p=.5511) for Taeeumin, 0.629 (p<.0001) for Soeumin and 0.604(p<.0001) for Soyangin, respectively. so the classification accuracy for Soeumin was highest Soeumin for over 30 points and Soyangin for below 28 points respectively. Conclusions : We suggest that Taeeumin is not classified easily in the ROC-curve analysis. We may classify Soeumin and Soyangin but the accuracy of Sasang constitutional diagnosis is still low.

• Korean Circulation Journal
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• v.52 no.5
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• pp.368-378
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• 2022

Background and Objectives: Recurrence rates after radiofrequency catheter ablation (RFCA) in atrial fibrillation (AF) patients are not low especially in non-paroxysmal AF. The diameter of left atrium (LA) has been widely used to predict the recurrence after RFCA for decades. However, LA diameter represents structural remodeling of LA and does not reflect electrical remodeling. We aimed to determine the predictive value of electrical remodeling of LA which is represented by the amount of low voltage zone (LVZ). Methods: We performed a retrospective cohort analysis of AF patients who underwent de novo RFCA in a single-center. Results: A total of 3,120 AF patients with de novo RFCA were analyzed. Among these patients, 537 patients underwent an electroanatomic mapping with bipolar voltage measurement of LA. The diameter of LA and flow velocity of LA appendage (LAA) differed significantly according to quartile group of LVZ area and percentage: patients with high LVZ had large LA diameter and low LAA flow velocity (p<0.001). Freedom from late recurrence (LR) was significantly lower in patients with high LVZ area and percentage (p<0.001). The diameter and surface area of LA had area under curve (AUC) of 0.592 and 0.593, respectively (p=0.002 for both). The predictive value of LVZ area (AUC, 0.676) and percentage (AUC, 0.671) were both superior compared with LA diameter (p=0.011 and 0.027 for each comparison). Conclusions: In conclusion, LVZ can predict freedom from LR after RFCA in AF patients. Predictive value was higher in parameters reflecting electrical rather than structural remodeling of LA.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.305-332
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• 2022

Purpose: As the public interest in entrepreneurship has been highlighted and entrepreneurship policies have been generated, this study is to construct Entrepreneurship Ecosystem (EE) models which have a significant relationship to national entrepreneurship with quantitative analysis. It aims to provide implications to EE policymakers that which national components are effective in cultivating innovative entrepreneurship and validate its EE quality based on quantitative performance goals. Methods: This study utilizes secondary data, categorized under the PESTLE factor from credible international organizations (WB, UNDP, GEM, GEDI, and OECD) to determine significant factors in the quality of the entrepreneurial ecosystem. This paper uses the Multiple Linear Regression (MLR) analysis to select the significant variables contributing to entrepreneurship performance. Using the AUC-ROC performance evaluation method for machine learning MLR results, this paper evaluates the performance of EE models so that it can allow approving EE quality by predicting potential performance. Results: Among nine hypothesis models, MLR analysis examines that the number of the Unicorn company, Unicorn companies' economic value, and entrepreneurship measured as GEI can be reasonable dependent variables to indicate the performance derived from EE quality. Rather than government policies and regulations, the social, finance, technology, and economic variables are significant factors of EE quality determining its performance. By having high Area Under Curve values under AUC-ROC analysis, accepted MLR models are regarded as having high prediction accuracy. Conclusion: Superior EE contributes to the outstanding Unicorn companies, and improvement in macro-environmental components can enhance EE quality.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.325-334
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• 2016

We projected the distribution of Hedera rhombea, an evergreen broad-leaved climbing plant, under current climate conditions and predicted its future distributions under global warming. Inaddition, weexplained model uncertainty by employing 9 single Species Distribution model (SDM)s to model the distribution of Hedera rhombea. 9 single SDMs were constructed with 736 presence/absence data and 3 temperature and 3 precipitation data. Uncertainty of each SDM was assessed with TSS (Ture Skill Statistics) and AUC (the Area under the curve) value of ROC (receiver operating characteristic) analyses. To reduce model uncertainty, we combined 9 single SDMs weighted by TSS and resulted in an ensemble forecast, a TSS weighted ensemble. We predicted future distributions of Hedera rhombea under future climate conditions for the period of 2050 (2040~2060), which were estimated with HadGEM2-AO. RF (Random Forest), GBM (Generalized Boosted Model) and TSS weighted ensemble model showed higher prediction accuracies (AUC > 0.95, TSS > 0.80) than other SDMs. Based on the projections of TSS weighted ensemble, potential habitats under current climate conditions showed a discrepancy with actual habitats, especially in the northern distribution limit. The observed northern boundary of Hedera rhombea is Ulsan in the eastern Korean Peninsula, but the projected limit was eastern coast of Gangwon province. Geomorphological conditions and the dispersal limitations mediated by birds, the lack of bird habitats at eastern coast of Gangwon Province, account for such discrepancy. In general, potential habitats of Hedera rhombea expanded under future climate conditions, but the extent of expansions depend on RCP scenarios. Potential Habitat of Hedera rhombea expanded into Jeolla-inland area under RCP 4.5, and into Chungnam and Wonsan under RCP 8.5. Our results would be fundamental information for understanding the potential effects of climate change on the distribution of Hedera rhombea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.374-382
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• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.