• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Economic and Environmental Geology
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• v.46 no.4
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• pp.301-312
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• 2013

Landslides are caused by complex interaction among a large number of interrelated factors such as topography, geology, forest and soils. In this study, a comparative study was carried out using fuzzy relationship method and artificial neural network to evaluate landslide susceptibility. For landslide susceptibility mapping, maps of the landslide occurrence locations, slope angle, aspect, curvature, lithology, soil drainage, soil depth, soil texture, forest type, forest age, forest diameter and forest density were constructed from the spatial data sets. In fuzzy relation analysis, the membership values for each category of thematic layers have been determined using the cosine amplitude method. Then the integration of different thematic layers to produce landslide susceptibility map was performed by Cartesian product operation. In artificial neural network analysis, the relative weight values for causative factors were determined by back propagation algorithm. Landslide susceptibility maps prepared by two approaches were validated by ROC(Receiver Operating Characteristic) curve and AUC(Area Under the Curve). Based on the validation results, both approaches show excellent performance to predict the landslide susceptibility but the performance of the artificial neural network was superior in this study area.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.342-348
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• 2011

The study aimed to compare the validity between the abdominal ultrasonographic(US) grading system of fatty liver and histologic grading system of fatty liver in living liver donor candidates. As the fatty liver is defined as pathologic total fat >10%, US validity was sensitivity 64.6%, specificity 68%, positive predictive value 76.8%, negative predictive value 54%. As the strict data handling on US grading normal, mild fatty liver are negative, moderate fatty liver is positive, US validity was sensitivity 26.8%, specificity 100%, positive predictive value 100%, negative predictive value 45.5%. ROC curve analysis according to different cut off value of liver-to-kidney brightness ratio was Area under ROC curve=0.859(95% CI=0.795~0.922, state variable= total fat 10%). There were statistically significant difference( p<0.001). Ultrasonography for the fatty diagnosis showed a high validity to predict the result of histology grade of fatty liver.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1447-1452
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• 2012

Aim: To evaluate the differential diagnostic potential of lesion stiffness assessed by the sonoelastographic strain index ratio (SR) and elastographic color scoring system (UE) for breast lesions. Materials and Methods: Three hundred and forty two breast masses (158 benign and 184 malignant) from 325 consecutive patients (mean age 44.2 years; range 16-81)who had been scheduled for a sonographically guided core biopsy were examined proposed by Itoh et al, with scoring 1-3=benign and 4-5=malignant. Strain and area ratios of each lesion were calculated within the same machine. Histological diagnosis was used as the reference standard. The area under the curve (AUC) and cut-off point were obtained by receiver operating curve and the cross table Fischer Test was carried out for assessing diagnostic value. Sensitivity, specificity, PPV, NPV, accuracy and false-discovery rates were compared. Results: The mean strain ratios for benign and malignant lesions were 1.87 and 7.9 respectively. (P<0.0001). When a cutoff point of 3.54 was used, SR had a sensitivity of 94.6%, a specificity 94.3%, a PPV of 95.1%, an NPV of 93.7% and an accuracy of 94.4%. The AUC values were 0.90 for the 5 point scoring system (UE) and 0.96 for the strain index ratio. The overall diagnostic performance was SR method was better (P<0.05). Conclusions: Strain ratio measurement could be another effective predictor in elastography imaging besides 5 the point scoring system for differential diagnosis of breast lesions.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.367-367
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• 2019

최근 기후변화로 인해 국지성 호우 및 태풍의 빈도가 빈발하고 및 규모가 커지고 있으며 그로 인한 홍수피해규모는 증가하고 있다. 본 논문에서는 도시 지역의 호우로 인한 침수유발 강우량을 산정하는 기법의 정확도를 산정하는데 목적이 있으며 이를 위해 ROC(Receiver Operation Characteristic Curve) 분석을 이용하였다. 본 논문에서는 분포형 홍수해석 모형인 S-RAT 모형과 2차원 침수해석 모형 FLO-2D을 커플링하여 호우로 인한 침수해석을 실시하였으며 강우시나리오는 설계 강우 200mm의 강우를 10% 간격으로 증가시켜 강우량 대비 침수심 자료를 모의하였다. 모의한 침수심 자료를 이용하여 유역 격자를 $1km{\times}1km$ 별 강우량-침수심 관계곡선식을 제시하였으며 개발된 곡선식을 이용하여 특정 침수심(20cm)을 유발시키는 강우량(한계강우량)을 산정하였다. 정확도 산정은 ROC(Receiver Operation Characteristic Curve) 분석 방법을 이용하여 침수 유무의 적중률에 따른 민감도와 특이도를 이용하여 AUC(Area Under the Curve)의 점수로 정확도를 판단하였다. 본 논문에서는 본 논문에서 제시한 한계강우량의 정확도를 판단하기 위하여 2011년 7월의 사당역 일대 침수사례를 이용하였다. 실제 침수정보가 없어 실제 호우사상과 실제 하수관망을 고려할 수 있는 SWMM 모형을 이용하여 침수분석을 실시하였다. 분석 결과 평균 ROC는 약 0.7로 나타났으며 5 단계의 구분에서 Fair 단계로 적정 수준의 정확도를 확보한 것으로 나타났다.

• Journal of the Korean Society of Physical Medicine
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• v.12 no.2
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• pp.129-136
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• 2017

PURPOSE: The purpose of this study was to compare the gait of elderly patients with and without dementia to investigate the possibility of an ambulation assessment test as a diagnostic tool for dementia. METHODS: A total of 96 subjects were included with 60 participants without dementia (control group) and 36 patients with dementia (dementia group). To compare the walking ability of the two groups, a 4-m walking test (4MWT) and Groningen Meander Walking Test (GMWT) were conducted. The GMWT is graded by amount of time in seconds and by number of oversteps outside the track. Mann-Whitney U test was used to compare the gait between the groups and the area under the curve (AUC) with Received Operating Characteristic (ROC) curve was analyzed. Statistical significance was considered at a p<.05, with a 95% confidence interval. RESULTS: There were statistically significant differences (p<.05) between the dementia group and the control group for the 4MWT, GMWTSEC, and GMWTSTEP scores. The AUC was .95 for 4MWT, .92 for GMWTSEC, and .96 for GMWTSTEP with the 95% confidence interval. The cut-off values of the ROC curve were 1.03m/s for 4MWT, 10.8 second for GMWTSEC, and 3.75 steps for GMSTEP. CONCLUSION: In our study, we investigated the utility of ambulatory assessment tools to predict dementia. The results of this study suggest that the 4MWT and the GMWT used in this study are appropriate assessment tools for dementia prediction.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5517-5523
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• 2012

Purpose: To evaluate the diagnostic accuracy of ultrasonograph and fine-needle aspiration cytologic examination (USG-FNAC) in the staging of axillary lymph node metastasis in breast cancer patients.Methods: We conducted an electronic search of the literature addressing the performance of USG-FNAC in diagnosis of axillary lymph node metastasis in databases such as Pubmed, Medline, Embase, Ovid and Cochrane library. We introduced a series of diagnostic test indices to evaluate the performance of USG-FNAC by the random effect model (REM), including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios and area under the curve (AUC). Results: A total of 20 studies including 1371 cases and 1289 controls were identified. The pooled sensitivity was determined to be 0.66 (95% CI 0.64-0.69), specificity 0.98 (95% CI 0.98-0.99), positive likelihood ratio 22.7 (95% CI 15.0-34.49), negative likelihood ratio 0.32 (95% CI 0.25-0.41), diagnostic OR 84.2 (95% CI 53.3-133.0). Due to the marginal threshold effect found in some indices of diagnostic validity, we used a summary SROC curve to aggregate data, and obtained a symmetrical curve with an AUC of 0.942. Conclusion: The results of this meta-analysis indicated that the USG-FNAC techniques have acceptable diagnostic validity indices and can be used for early staging of axillary lymph node in breast cancer patients.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.129-136
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• 2021

Purpose: This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjögren syndrome patients. Materials and Methods: This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases. The 3 radiologists (observers A, B, and C) evaluated 400 static US images of salivary glands twice at a 1-year interval. To compare their performance, 2 experienced radiologists evaluated the same images. Diagnostic performance was compared between the 2 evaluations using the area under the receiver operating characteristic curve (AUC). Results: Observer A, who was participating in the training program for the second year, exhibited no significant difference in AUC between the first and second evaluations, with results consistently comparable to those of experienced radiologists. After 1 year of training, observer B showed significantly higher AUCs than before training. The diagnostic performance of observer B reached the level of experienced radiologists for parotid gland assessment, but differed for submandibular gland assessment. For observer C, who did not complete the training, there was no significant difference in the AUC between the first and second evaluations, both of which showed significant differences from those of the experienced radiologists. Conclusion: These preliminary results suggest that the training program effectively helped inexperienced radiologists reach the level of experienced radiologists for US examinations.

• Korean Journal of Biological Psychiatry
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• v.27 no.2
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• pp.94-100
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• 2020

Objectives Amyloid β positron emission tomography (Aβ PET) is widely used as a diagnostic tool in patients who have symptoms of cognitive impairment, however, this diagnostic examination is too expensive. Thus, predicting the positivity of Aβ PET before patients undergo the examination is essential. We aimed to analyze clinical predictors of patients who underwent Aβ PET retrospectively, and to develop a predicting model of Aβ PET positivity. Methods 468 patients who underwent Aβ PET with cognitive impairment were recruited and their clinical indicators were analyzed retrospectively. We specified the primary outcome as Aβ PET positivity, and included variables such as age, sex, body mass index, diastolic blood pressure, systolic blood pressure, education, dementia family history, Mini Mental Status Examination (MMSE), Clinical Dementia Rating (CDR), Clinical Dementia Rating-Sum of Box (CDR-SB), hypertension (HTN), diabetes mellitus (DM) and presence of apolipoprotein E (ApoE) E4 as potential predictors. We developed three final models of amyloid positivity prediction for total subjects, mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia using a multivariate stepwise logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed and the area under curve (AUC) value was calculated for the ROC curve. Results Aβ PET negative patients were 49.6% (n = 232), and Aβ PET positive patients were 50.4% (n = 236). In the final model of all subjects, older age, female sex, presence of ApoE E4 and lower MMSE are associated with Aβ PET positivity. The AUC value was 0.296. In the final model of MCI subjects (n = 244), older age and presence of ApoE E4 are associated with Aβ PET positivity. The AUC value was 0.725. In the final model of AD subjects (n = 173), lower MMSE scores, the presence of ApoE E4 and history of HTN are associated with Aβ PET positivity. The AUC value was 0.681. Conclusions The cerebral amyloid positivity model, which was based on commonly available clinical indicators, can be useful for prediction of amyloid PET positivity in MCI or AD patients.