• Journal of Preventive Medicine and Public Health
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• v.34 no.4
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• pp.408-416
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• 2001

Objectives : To evaluate the diagnostic value of quantitative ultrasound (QUS) in the prediction of osteoporosis as defined by dual energy x-ray absorptiometry (DEXA) in postmenopausal women. Methods : Questionnaires and height and weight measurements were used in the investigation of 176 postmenopausal women. QUS measurements were taken on the right calcaneus while bone mineral density (BMD) measurements of the lumbar spine and femoral neck were made with DEXA. The areas under the curves (AUC) of the speed of sound (SOS) for osteoporosis in the lumbar spine and femoral neck were obtained through receiver operating characteristic (ROC) analysis and evaluated. A comparison was made, for osteoporosis in the lumbar spine and femoral neck, between the AUCs of the logistic model with clinical risk factors and SOS. Results : Pearson's correlation coefficients of SOS and lumbar spine BMD, and of SOS and femoral neck BMD were 0.26 and 0.37. The AUC for the logistic model in its discrimination for lumbar spine osteoporosis was 0.764, and for SOS 0.605. The AUCs for the logistic model in its discrimination for femoral neck osteoporosis and for SOS were 0.890 and 0.892, respectively. Conclusions : These results suggest that the diagnostic value of QUS as a screening tool for osteoporosis is moderate for the femoral neck, but merely low for the lumbar spine and that the predictability provided by SOS is no better than that by the sole use of clinical risk factors in postmenopausal women.

• 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.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.484-491
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• 2018

Primer $Sj{\ddot{o}}gren's$ Syndrome (pSS) is an autoimmune/inflammatory illness. The platelet indices (PIs) indicate the inflammatory response and activity/severity of many diseases. A vitamin D deficiency is accompanied by the increased tendency of autoimmune diseases. This study investigated whether the vitamin D levels are related to the altered platelet indices in pSS. A total of 261 individuals were included in this analytical cross-sectional study. The laboratory data of pSS patients were evaluated and the relationship between the PIs and vitamin D status was examined. According to these findings, in patients with pSS, the vitamin D levels were lower than the healthy control group (P<0.05). The vitamin D levels were negatively associated with PDW (P=0.012), but positively correlated with PCT (P<0.001). The cut-off point was obtained with receiver operating characteristics (ROC) curves for PDW: 12.53 (AUC 0.921, sensitivity 90%, specificity 85%), for PCT; 0.29 (AUC 0.660, sensitivity 68%, specificity 55%). In multivariate linear regression analysis, the most significant parameters for the effects of PDW are the following: vitamin D (${\beta}=-0.373$; t=-2.626; sig.=0.013) and plateletcrit (${\beta}=-0.308$; t=-2.13; sig.=0.040). A vitamin D deficiency may be accompanied by changes in PIs in pSS. A higher PDW and lower PCT supports the underlying inflammation, which may be vitamin D related useful parameters to consider in approaching to pSS.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.599-609
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• 2021

Data with a large difference in the number of objects between clusters are called unbalanced data. In discriminant analysis of unbalanced data, it is more important to classify objects in minority categories than to classify objects in majority categories well. However, objects in minority categories are often misclassified into majority categories. In this study, we propose a method that combined hierarchical DBSCAN (HDBSCAN) and SMOTE to solve this problem. Using HDBSCAN, it removes noise in minority categories and majority categories. Then it applies SMOTE to create new data. Area under the roc curve (AUC) and F1 scores were used to compare performance with existing methods. As a result, in most cases, the method combining HDBSCAN and synthetic minority oversampling technique (SMOTE) showed a high performance index, and it was found to be an excellent method for classifying unbalanced data.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.25-32
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• 2023

Recognizing the size and location of prostate cancer is critical for prostate cancer diagnosis, treatment, and predicting prognosis. This paper proposes a model to classify the tumor region and normal tissue with cross-sectional visual images of prostatectomy tissue. We used specimen images of 44 prostate cancer patients who received prostatectomy at Gachon University Gil Hospital. A total of 289 prostate slice images consist of 200 slices including tumor region and 89 slices not including tumor region. Images were divided based on the presence or absence of tumor, and a total of 93 features from each slice image were extracted using Radiomics: 18 first order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM. We compared feature selection techniques such as LASSO, ANOVA, SFS, Ridge and RF, LR, SVM classifiers for the model's high performances. We evaluated the model's performance with AUC of the ROC curve. The results showed that the combination of feature selection techniques LASSO, Ridge, and classifier RF could be best with an AUC of 0.99±0.005.

• Atmosphere
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• v.33 no.5
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• pp.531-548
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• 2023

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACI_SnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACI_YrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACI_SnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACI_YrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACI_SnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACI_YrOpt to ACI_SnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.1-16
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• 2024

Due to its complexity and high diagnosis and treatment costs, heart attack (HA) is the top cause of death globally. Heart failure's widespread effect and high morbidity and death rates make accurate and fast prognosis and diagnosis crucial. Due to the complexity of medical data, early and accurate prediction of HA is difficult. Healthcare providers must evaluate data quickly and accurately to intervene. This novel hybrid approach predicts HA using Long Short-Term Memory (LSTM) networks, Deep belief networks (DBNs) with attention mechanism, and robust data mining to fill this essential gap. HA is predicted using Kaggle, PhysioNet, and UCI datasets. Wearable sensor data, ECG signals, and demographic and clinical data provide a solid analytical base. To maintain consistency, ECG signals are normalized and segmented after thorough cleaning to remove missing values and noise. Feature extraction employs complex approaches like Principal Component Analysis (PCA) and Autoencoders to pick time-domain (MNN, SDNN, RMSSD, PNN50) and frequency-domain (PSD at VLF, LF, HF bands) characteristics. The hybrid model architecture uses LSTM networks for sequence learning and DBNs for feature representation and selection to create a robust and comprehensive prediction model. Accuracy, precision, recall, F1-score, and ROC-AUC are measured after cross-entropy loss and SGD optimization. The LSTM-DBN model outperforms predictive methods in accuracy, sensitivity, and specificity. The findings show that several data sources and powerful algorithms can improve heart attack predictions. The proposed architecture performed well on many datasets, with an accuracy rate of 96.00%, sensitivity of 98%, AUC of 0.98, and F1-score of 0.97. High performance proves this system's dependability. Moreover, the proposed approach is outperformed compared to state-of-the-art systems.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.11
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• pp.4603-4608
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• 2015

Background: To determine the potential value of serum tumor markers in predicting pCR (pathological complete response) during neoadjuvant chemotherapy. Materials and Methods: We retrospectively monitored the pro-, mid-, and post-neoadjuvant treatment serum tumor marker concentrations in patients with locally advanced breast cancer (stage II-III) who accepted pre-surgical chemotherapy or chemotherapy in combination with targeted therapy at Fudan University Shanghai Cancer Center between September 2011 and January 2014 and investigated the association of serum tumor marker levels with therapeutic effect. Core needle biopsy samples were assessed using immunohistochemistry (IHC) prior to neoadjuvant treatment to determine hormone receptor, human epidermal growth factor receptor 2(HER2), and proliferation index Ki67 values. In our study, therapeutic response was evaluated by pCR, defined as the disappearance of all invasive cancer cells from excised tissue (including primary lesion and axillary lymph nodes) after completion of chemotherapy. Analysis of variance of repeated measures and receiver operating characteristic (ROC) curves were employed for statistical analysis of the data. Results: A total of 348 patients were recruited in our study after excluding patients with incomplete clinical information. Of these, 106 patients were observed to have acquired pCR status after treatment completion, accounting for approximately 30.5% of study individuals. In addition, 147patients were determined to be Her-2 positive, among whom the pCR rate was 45.6% (69 patients). General linear model analysis (repeated measures analysis of variance) showed that the concentration of cancer antigen (CA) 15-3 increased after neoadjuvant chemotherapy in both pCR and non-pCR groups, and that there were significant differences between the two groups (P=0.008). The areas under the ROC curves (AUCs) of pre-, mid-, and post-treatment CA15-3 concentrations demonstrated low-level predictive value (AUC=0.594, 0.644, 0.621, respectively). No significant differences in carcinoembryonic antigen (CEA) or CA12-5 serum levels were observed between the pCR and non-pCR groups (P=0.196 and 0.693, respectively). No efficient AUC of CEA or CA12-5 concentrations were observed to predict patient response toward neoadjuvant treatment (both less than 0.7), nor were differences between the two groups observed at different time points. We then analyzed the Her-2 positive subset of our cohort. Significant differences in CEA concentrations were identified between the pCR and non-pCR groups (P=0.039), but not in CA15-3 or CA12-5 levels (p=0.092 and 0.89, respectively). None of the ROC curves showed underlying prognostic value, as the AUCs of these three markers were less than 0.7. The ROC-AUCs for the CA12-5 concentrations of inter-and post-neoadjuvant chemotherapy in the estrogen receptor negative HER2 positive subgroup were 0.735 and 0.767, respectively. However, the specificity and sensitivity values were at odds with each other which meant that improving either the sensitivity or specificity would impair the efficiency of the other. Conclusions: Serum tumor markers CA15-3, CA12-5, and CEA might have little clinical significance in predicting neoadjuvant treatment response in locally advanced breast cancer.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.125-136
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• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.4
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• pp.285-291
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• 2008

Purpose: Although automatic quantification software of myocardial perfusion SPECT provides highly objective and reproducible quantitative measurements, there is still some limitation in the direct use of quantitative measurements. In this study we derived parameters using normal variation of perfusion measurements, and tried to test the usefulness of these parameters. Materials and Methods: In order to calculate normal variation of perfusion measurements on myocardial perfusion SPECT, 55 patients (M:F = 28:27) of low-likelihood for coronary artery disease were enrolled and $^{201}TI$ rest/$^{99m}Tc$-MIBI stress SPECT studies were performed. Using 20-segment model, mean (m) and standard deviation (SD) of perfusion were calculated in each segment. As a myocardial viability assessment group, another 48 patients with known coronary artery disease, who underwent coronary artery bypass graft surgery (CABG) were enrolled. $^{201}TI$ rest/$^{99m}Tc$-MIBI stress / $^{201}TI$ 24-hr delayed SPECT was performed before CABG and SPECT was followed up 3 months after CABG. From the preoperative 24-hr delayed SPECT, $Q_{delay}$ (perfusion measurement), ${\Delta}_{delay}$ ($Q_{delay}$ - m) and $Z_{delay}$ (($Q_{delay}$ - m)/SD) were defined and diagnostic performances of them for myocardial viability were evaluated using area under curve (AUC) on receiver operating characteristic (ROC) curve analysis. Results: Segmental perfusion measurements showed considerable normal variations among segments. In men, the lowest segmental perfusion measurement was $51.8{\pm}6.5$ and the highest segmental perfusion was $87.0{\pm}5.9$, and they are $58.7{\pm}8.1$ and $87.3{\pm}6.0$, respectively in women. In the viability assessment $Q_{delay}$ showed AUC of 0.633, while those for ${\Delta}_{delay}$ and $Z_{delay}$ were 0.735 and 0.716, respectively. The AUCs of ${\Delta}_{delay}$ and $Z_{delay}$ were significantly higher than that of $Q_{delay}$ (p = 0.001 and 0.018, respectively). The diagnostic performance of ${\Delta}_{delay}$, which showed highest AUC, was 85% of sensitivity and 53% of specificity at the optimal cutoff of -24.7. Conclusion: On automatic quantification of myocardial perfusion SPECT, the normal variation of perfusion measurements were considerable among segments. In the viability assessment, the parameters considering normal variation showed better diagnostic performance than the direct perfusion measurement. This study suggests that consideration of normal variation is important in the analysis of measurements on quantitative myocardial perfusion SPECT.