• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1628-1633
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• 2017

The purpose of this study was to identify the difference and correlation in elbow joint maximal flexion strength according to measurement methods and characteristics of muscular contraction, and to develop the predictive equation of elbow joint maximal flexion strength for the optimal exercise intensity setting and accurate measurement. Subjects were 30 male university students. Elbow joint maximal flexion strength of isokinetic contraction, isometric contraction at $75^{\circ}$ elbow joint flexion position, isotonic concentric 1RM, manual muscle strength (MMT) were measured with isokinetic dynamometer, dumbbell, and manual muscle tester. Pearson's r, linear regression equation, and multiple regression equation between variables were calculated. As a result, the highest value was isometric contraction. The second highest value was MMT. The third highest value was isokinetic contraction. 1RM was the lowest. Predictive equations of elbow joint maximal flexion strength between isometric and isokinetic contraction, between isometric contraction and 1RM, among isometric contraction, 1RM, and body weight were developed. In conclusion, 1RM and isokinetic elbow joint maximal flexion strength could be seemed to underestimate the practical elbow joint maximal flexion strength. And it is suggested that the developed predictive equations in this study should be useful in criteria- and goal-setting for resistant exercise and sports rehabilitation after elbow joint injury.

• The Korean Journal of Cytopathology
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• v.2 no.1
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• pp.8-19
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• 1991

A statistical analysis of the diagnostic value for 244 aspiration biopsy cytology(ABC) among a total 1,043 cases from various sites was performed. ABC, using diagnostic terminology similar to that of a surgical pathology reports, was compared to the final tissue diagnosis. For the entire series, a sensitivity of 91.8%, a specificity of 99.3%, a positive predictive value of 98.9%, a negative predictive value of 94,8%, and an efficacy of the test of 96.3% were shown. There were 8 false negative and 1 false positive diagnosis. The diagnostic accuracy was 89.8%. Those results indicate that the ABC is a considerably highly accurate procedure that should be routinely employed.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.1015-1021
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• 2014

This study aimed at summarizing published study findings on the diagnostic value of rectal bleeding (RB) and informing clinical practice, preventive interventions and future research areas. We searched Medline and Embase for studies published by September 13, 2013 examining the risk of colorectal cancer in patients with RB using highly inclusive algorithms. Data for sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and positive predictive value (PPV) of RB were extracted by two researchers and analyzed applying Meta-Disc (version 1.4) and Stata (version 11.0). Methodological quality of studies was assessed according to QUADAS. A total of 38 studies containing 5,626 colorectal cancer patients and 73,174 participants with RB were included. The pooled sensitivity and specificity were 0.47 (95% CI: 0.45-0.48) and 0.96 (95% CI: 0.96-0.96) respectively. The overall PPVs ranged from 0.01 to 0.21 with a pooled value of 0.06 (95% CI: 0.05-0.08). Being over the age of 60 years, change in bowel habit, weight loss, anaemia, colorectal cancer among first-degree relatives and feeling of incomplete evacuation of rectum appeared to increase the predictive value of RB. Although RB greatly increases the probability of diagnosing colorectal cancer, it alone may not be sufficient for proposing further sophisticated investigations. However, given the high specificity, subjects without RB may be ruled out of further investigations. Future studies should focus on strategies using RB as an "alarm" symptom and finding additional indications to justify whether there is a need for further investigations.

• Journal of dental hygiene science
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• v.24 no.1
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• pp.22-28
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• 2024

Background: Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning. Methods: This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model. Results: As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM_2.5, PM₁₀, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy. Conclusion: In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM_2.5, PM₁₀, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.602-610
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• 2018

The model predictive control is an effective method to optimize the current control input that predicts the current control state and the future error using the predictive model of the control system when the reference trajectory is known. Since the control input can not have a physically infinitely large value, a predictive controller design with constraints should be considered. In addition, the reference model $A_r$ and the weight matrices Q, R that determine the control performance of the predictive controller are not optimized as arbitrarily designated should be considered in the controller design. In this study, we construct a predictive controller of a mobile robot by transforming it into a quadratic programming problem with constraints, The control performance of the mobile robot can be improved by optimizing the control parameters of the predictive controller that determines the control performance of the mobile robot using genetic algorithm. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

• The Journal of Korean Orthopaedic Ultrasound Society
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• v.4 no.1
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• pp.1-6
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• 2011

Purpose: To evaluate the usefulness of ultrasonography in diagnosis of the medial meniscus tear as a screening tool before performing magnetic resonance imaging. Materials and Methods: From April 2009 to September 2010, magnetic resonance imaging (MRI) was taken in 147 knees out of 341 knees examined with ultrasonography (US) under the suspicion of medial meniscus tear. The sonographic findings were 16 without abnormality, 12 inhomogeneity, 4 cluster, 60 cleavage and 55 more than 5mm medial extrusion of medial meniscus. In Statistical analysis, sensitivity and specificity, positive predictive value and negative predictive values were calculated. Results: The MRI showed abnormality in 104 knees. Sensitivity and specificity of ultrasonography for MRI was 94.2% and 23.3%, respectively. Positive predictive value was 74.8%, negative predictive value was 62.5%. The positive predictive value of ultrasonography were 58.3% in heterogeneity, 100% in showing cluster, 75% in visible cleft and 80% in medial extrusion. Conclusion: The US is a useful tool in prediction of medial meniscus tear before confirming it in high-cost MRI.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.2089-2092
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• 2016

Background: The impact of mean platelet volume (MPV) on prognosis, diagnosis and response to therapy in cancer patients has been widely investigated. In the present study, we evaluated whether MPV at diagnosis has predictive value for pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with locally advanced breast cancer (LABC). Materials and Methods: A total of 109 patients with LABC from Akdeniz University and Antalya Research and Training Hospital were evaluated retrospectively. Results: ROC curve analysis suggested that the optimum MPV cut-off point for LABC patients with pCR (+) was 8.15 (AUC:0.378, 95%CI [0.256-0.499], p=0.077). The patients with MPV <8.15 had higher pCR rates (29.2% vs. 13.1%, p=0.038). After binary logistic regression analysis, MPV and estrogen receptor absence were independent predictors for pCR. Conclusions: MPV has an independent predictive value for pCR after neoadjuvant chemotherapy in patients with LABC.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.31-40
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• 2020

This study investigates whether other comprehensive income (OCI) reported in the statement of comprehensive income (one of the main financial statements after the adoption of K-IFRS) predicts a firm's future performance. Using the quarterly data of Korean listed companies, we examine the association between OCI estimates and future earnings. First of all, we find that OCI is positively associated with earnings in both 1- and 2-quarter ahead, supporting the predictive value of OCI. When we break down OCI into its individual components, our results suggest that the net unrealized gains/losses on available-for-sale (AFS) investment securities are positively associated with future earnings, while the other components (e.g., net unrealized gains/losses on valuation of cash flow hedge derivatives) present insignificant results. In addition, we investigate whether the reliability in OCI estimates enhances the predictive value of OCI to predict future performance. We find that the predictive ability of OCI, in particular the net unrealized gains/losses on available-for-sale (AFS) investment securities, becomes more pronounced when firms are audited by the Big 4 audit firms. Overall, our study suggests that information content embedded in OCI can provide decision-useful information that is helpful for the prediction of future firm performance.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.345-349
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• 2015

Background: To determine the cut-off values of the preoperative risk of malignancy index (RMI) used in differentiating benign or malignant adnexal masses and to determine their significance in differential diagnosis by comparison of different systems. Materials and Methods: 191 operated women were assessed retrospectively. RMI of 1, 2, 3 and 4; cut-off values for an effective benign or malignant differentiation together with sensitivity, specificity, negative and positive predictive values were calculated. Results: Cut-off value for RMI 1 was found to be 250; there was significant (p<0.001) compatibility at this level with sensitivity of 60%, positive predictive value (PPV) of 75%, specificity of 93%, negative predictive value (NPV) of 88% and an overall compliance rate of 85%. When RMI 2 and 3 was obtained with a cut-off value of 200, there was significant (p<0.001) compatibility at this level for RMI 2 with sensitivity of 67%, PPV of 67%, specificity of 89%, NPV of 89%, histopathologic correlation of 84% while RMI 3 had significant (p<0.001) compatibility at the same level with sensitivity of 63%, PPV of 69%, specificity of 91%, NPV of 88% and a histopathologic correlation of 84%. Significant (p<0.001) compatibility for RMI 4 with a sensitivity of 67%, PPV of 73%, specificity of 92%, NPV of 89% and a histopathologic correlation of 86% was obtained at the cut-off level 400. Conclusions: RMI have a significant predictability in differentiating benign and malignant adnexal masses, thus can effectively be used in clinical practice.