• Journal of Environmental Impact Assessment
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• v.22 no.3
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• pp.219-230
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• 2013

Species distribution modeling is one of the most effective habitat analysis methods for wildlife conservation. This study was for evaluating the suitability of species distribution to distance between forest patches in Seoul city using tits. We analyzed the distribution of the four species of tits: varied tit (Parus varius), marsh tit (P. palustris), great tit (P. major) and coal tit (P. ater), using the landscape indexes and connectivity indexes, and compared the resulting suitability indexes from 100m to 1,000m. As factors affecting to the distribution of tits, we calculated landscape indices by separating them into intra-patch indices (i.e. logged patch area (PA), area-weighted mean patch shape index (PSI), tree rate (TR)) and inter-patch indices (i.e. patch degree (PD), patch betweenness (PB), difference probability of connectivity (DPC)), to analyze the internal properties of the patches and their connectivity by tits occurrence data using logistic regression modeling. The models were evaluated by AICc (Akaike Information Criteria with a correction for finite sample sizes) and AUC (Area Under Curve of ROC). The results of AICc and AUC showed DPC, PA, PSI, and TR were important factors of the habitat models for great tit and marsh tit at the level of distance 500~800m. In contrast, habitat models for coal tit and varied tit, which are known as forest interior species, reflected PA, PSI, and TR as intra-patch indices rather than connectivity. These mean that coal tit and varied tit are more likely to find a large circular forest patch than a small and long-shaped forest patch, which are higher rate of forest. Therefore, different strategies are required in order to enhance the habitats of the forest birds, tits, in a region that has fragmented forest patches such as Seoul city. It is important to manage forest interior areas for coal tit and varied tit, which are known as forest interior species and to manage not only forest interior areas but also connectivity of the forest patches in the threshold distance for great tit and marsh tit as adapted species to the urban ecosystem for sustainable ecosystem management.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Korean Journal of Radiology
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• v.24 no.5
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• pp.395-405
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• 2023

Objective: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

• Journal of The Korean Society of Clinical Toxicology
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• v.19 no.2
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• pp.72-82
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• 2021

Purpose: A high anion gap (AG) is known to be a significant risk factor for serious acid-base imbalances and death in acute poisoning cases. The strong ion difference (SID), or strong ion gap (SIG), has recently been used to predict in-hospital mortality or acute kidney injury (AKI) in patients with systemic inflammatory response syndrome. This study presents a comprehensive acid-base analysis in order to identify the predictive value of the SIG for disease severity in severe poisoning. Methods: A cross-sectional observational study was conducted on acute poisoning patients treated in the emergency intensive care unit (ICU) between December 2015 and November 2020. Initial serum electrolytes, base deficit (BD), AG, SIG, and laboratory parameters were concurrently measured upon hospital arrival and were subsequently used along with Stewart's approach to acid-base analysis to predict AKI development and in-hospital death. The area under the receiver operating characteristic curve (AUC) and logistic regression analysis were used as statistical tests. Results: Overall, 343 patients who were treated in the intensive care unit were enrolled. The initial levels of lactate, AG, and BD were significantly higher in the AKI group (n=62). Both effective SID [SIDe] (20.3 vs. 26.4 mEq/L, p<0.001) and SIG (20.2 vs. 16.5 mEq/L, p<0.001) were significantly higher in the AKI group; however, the AUC of serum SIDe was 0.842 (95% confidence interval [CI]=0.799-0.879). Serum SIDe had a higher predictive capacity for AKI than initial creatinine (AUC=0.796, 95% CI=0.749-0.837), BD (AUC=0.761, 95% CI=0.712-0.805), and AG (AUC=0.660, 95% CI=0.607-0.711). Multivariate logistic regression analyses revealed that diabetes, lactic acidosis, high SIG, and low SIDe were significant risk factors for in-hospital mortality. Conclusion: Initial SIDe and SIG were identified as useful predictors of AKI and in-hospital mortality in intoxicated patients who were critically ill. Further research is necessary to evaluate the physiological nature of the toxicant or unmeasured anions in such patients.

• Journal of The Korean Society of Clinical Toxicology
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• v.16 no.2
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• pp.61-67
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• 2018

Purpose: To evaluate the association between neutrophil-to-lymphocyte ratio (NLR) and occurrence of aspiration pneumonia in drug intoxication (DI) patients in the emergency department (ED) and to evaluate the relationship between NLR and length of hospital admission/intensive care unit (ICU) admission Methods: A total of 466 patients diagnosed with DI in the ED from January 2016 to December 2017 were included in the analysis. The clinical and laboratory results, including NLR, were evaluated as variables. NLR was calculated as the absolute neutrophil count/absolute lymphocyte count. To evaluate the prognosis of DI, data on the development of aspiration pneumonia were obtained. Also, we evaluated the relationship between NLR and length of hospital admission and between NLR and length of ICU admission. Statistically, multivariate logistic regression analyses, receiver-operating characteristic (ROC) curve analysis, and Pearson's correlation (${\rho}$) were performed. Results: Among the 466 DI patients, 86 (18.5%) developed aspiration pneumonia. Multivariate logistic regression analysis revealed NLR as an independent factor in predicting aspiration pneumonia (odds ratio, 1.7; p=0.001). NLR showed excellent predictive performance for aspiration pneumonia (areas under the ROC curves, 0.815; cut-off value, 3.47; p<0.001) with a sensitivity of 86.0% and a specificity of 72.6%. No correlations between NLR and length of hospital admission (${\rho}=0.195$) and between NLR and length of ICU admission (${\rho}=0.092$) were observed. Conclusion: The NLR is a simple and effective marker for predicting the occurrence of aspiration pneumonia in DI patients. Emergency physicians should be alert for aspiration pneumonia in DI patients with high NLR value (>3.47).

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.456-465
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• 2023

Melon fruits exhibit a wide range of morphological variations in fruit shape, sugar content, net quality, diameter and weight, which are largely dependent on the variety. These characteristics significantly affect marketability. For netted varieties, the uniformity and pattern of the net serve as key factors in determining the external quality of the melon and act as indicators of its internal quality. In this study, we evaluated the effect of fruit morphology and growth on netting by analyzing the changes in melon fruit quality under LED light treatment and monitoring fruit growth. Computer vision analysis was used for quantitative evaluation of fruit net quality, and a three-variable logistic model was applied to simulate fruit growth. The results showed that melons grown under LED conditions exhibited more uniform fruit shape and improvements in both net quality and sugar content compared to the control group. The results of the logistic model showed minimal error values and consistent curve slopes across treatments, confirming its ability to accurately predict fruit growth patterns under varying light conditions. This study provides an understanding of the effects of fruit shape and growth on net quality.

• Korean Journal of Radiology
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• v.24 no.2
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• pp.133-144
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• 2023

Objective: Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas. Materials and Methods: Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation. Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72-0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60-0.88), 87.8%, 80.0%, and 58.1%, respectively. Conclusion: The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.

• Journal of Trauma and Injury
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• v.37 no.3
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• pp.201-208
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• 2024

Purpose: The number of elderly patients with trauma is increasing; therefore, precise models are necessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict 30-day mortality in severely injured elderly patients with trauma and to compare the predictive performance of various machine learning models. Methods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visited the regional trauma center at Chungbuk National University Hospital between 2016 and 2022. Four machine learning models-logistic regression, decision tree, random forest, and eXtreme Gradient Boosting (XGBoost)-were developed to predict 30-day mortality. The models' performance was compared using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, specificity, F1 score, as well as Shapley Additive Explanations (SHAP) values and learning curves. Results: The performance evaluation of the machine learning models for predicting mortality in severely injured elderly patients with trauma showed AUC values for logistic regression, decision tree, random forest, and XGBoost of 0.938, 0.863, 0.919, and 0.934, respectively. Among the four models, XGBoost demonstrated superior accuracy, precision, recall, specificity, and F1 score of 0.91, 0.72, 0.86, 0.92, and 0.78, respectively. Analysis of important features of XGBoost using SHAP revealed associations such as a high Glasgow Coma Scale negatively impacting mortality probability, while higher counts of transfused red blood cells were positively correlated with mortality probability. The learning curves indicated increased generalization and robustness as training examples increased. Conclusions: We showed that machine learning models, especially XGBoost, can be used to predict 30-day mortality in severely injured elderly patients with trauma. Prognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality in elderly patients with severe trauma.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.61-72
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• 2011

In the benefits of ITS, there are intangible gains from real-time traffic information as well as classical gains such as travel time saving. These intangible gains are difficult to be estimated by existing transportation investment appraisal commonly used in SOC investment. The major reason is not because of the absence of methodology but because of the absence of generalized values of particular benefits from real time traffic information. This research explores the value of real-time traffic information on VMS that is the most representative of ITS services, by using CVM with Double Bounded Dichotomous Choice Question. Willingness-To-Pay (WTP) functions of drivers are built with survival functions using various types of probability distribution functions such as Exponential, Log-logistic, and Weibull functions. The results reveal that Log-logistic distribution is the most appropriate distribution model to estimate WTP, and the estimated coefficients are stable through LR (Likelihood Ratio) test. For the further study, it is recommended to perform statistical tests of temporal and spatial transferability that is not examined in this research due to the lack of data.