• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.285-295
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• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• Earthquakes and Structures
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• v.25 no.3
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• pp.187-198
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• 2023

Model Predictive Control (MPC) is an advanced control approach that uses the current states of the system model to predict its future behavior. In this article, according to the seismic dynamics of structural systems, the Predictive Functional Control (PFC) method is used to solve the control problem. Although conventional PFC is an efficient control method, its performance may be impaired due to problems such as uncertainty in the structure of state sensors and process equations, as well as actuator saturation. Therefore, it requires the utilization of appropriate estimation algorithms in order to accurately evaluate responses and implement actuator saturation. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering simultaneously the saturation actuator. Accordingly, an extended PFC is presented based on the H-ifinity (H∞) filter (HPFC) while considering the saturation actuator. Thus, the structural responses are formulated by two estimation models using the H∞ filter. First, the H∞ filter estimates responses using a performance bound (𝜃). Second, the H∞ filter is converted into a Kalman filter in a special case by considering the 𝜃 equal to zero. Therefore, the scheme based on the Kalman filter (KPFC) is considered a comparative model. The proposed method is evaluated through numerical studies on a building equipped with an Active Tuned Mass Damper (ATMD) under near and far-field earthquakes. Finally, HPFC is compared with classical (CPFC) and comparative (KPFC) schemes. The results show that HPFC has an acceptable efficiency in boosting the accuracy of CPFC and KPFC approaches under earthquakes, as well as maintaining a descending trend in structural responses.

• Physical Therapy Rehabilitation Science
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• v.12 no.2
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• pp.80-91
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• 2023

Objective: The purpose of this study is to identify factors that affect the incidence of hypertension using logistic regression and decision tree analysis, and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 9,859 subjects from the Korean health panel annual 2019 data provided by the Korea Institute for Health and Social Affairs and National Health Insurance Service. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In logistic regression analysis, those who were 60 years of age or older (Odds ratio, OR=68.801, p<0.001), those who were divorced/widowhood/separated (OR=1.377, p<0.001), those who graduated from middle school or younger (OR=1, reference), those who did not walk at all (OR=1, reference), those who were obese (OR=5.109, p<0.001), and those who had poor subjective health status (OR=2.163, p<0.001) were more likely to develop hypertension. In the decision tree, those over 60 years of age, overweight or obese, and those who graduated from middle school or younger had the highest probability of developing hypertension at 83.3%. Logistic regression analysis showed a specificity of 85.3% and sensitivity of 47.9%; while decision tree analysis showed a specificity of 81.9% and sensitivity of 52.9%. In classification accuracy, logistic regression and decision tree analysis showed 73.6% and 72.6% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. It is thought that both analysis methods can be used as useful data for constructing a predictive model for hypertension.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.26 no.5
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• pp.462-469
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• 2000

Positron Emission Tomography(PET) is a new diagnostic method that can create functional images of the distribution of positron emitting radionuclides, which when administered intravenously in the body, makes possible anatomical and functional analysis by quantity of biochemical and physiological process. After genetic and biochemical changes in initial stage, malignant tumor undergoes functional changes before undergoing anatomical changes. So, early diagnosis of malignant tumors by functional analysis with PET can be achieved, replacing traditional anatomical analysis, such as computed tomography(CT) and magnetic resonance image(MRI), etc. Similarly, PET can identify malignant tumor without confusion with scar and fibrosis in follow up check. In the Korea Cancer Center Hospital(KCCH) from October 1997 to September 1999, clinical study was performed in 79 cases that underwent 89 times PET evaluation with [18F]-Fluorodeoxyglucose for diagnosis of oral and maxillofacial tumors, and the data was analysed by Bayesian $2{\times}2$ Classification Table. The results were as follows : Evaluation for initial diagnosis with FDG-PET (P<0.005) 1. Agreement rate or accuracy rate is 88.9%. 2. Sensitivity is 95.2%, and specificity 66.7%. 3. Positive predictive rate is 90.9%, and negative predictive rate 80.0%. 4. In consideration of tumor stage, diagnostic rate in less than stage II was 90% and in greater than stage III 100%. 5. In consideration of tumor size, diagnostic rate in less than T2 was 92.3% and in greater than T3 100%. After primary treatment, evaluation for follow up check with FDG-PET (P < 0.001) 1. Agreement rate or accuracy rate is 85.4%. 2. Sensitivity is 87.5%, and specificity 82.4%. 3. Positive predictive rate is 87.5%, and negative predictive rate 82.4%. 4. In 24 recurred cases, 6 had distant metastasis, and 5 of them were diagnosed with FDG-PET, resulting in diagnostic rate of FDG-PET of 83.3%. From the above results, Positron Emission Tomography with [18F]- Fluorodeoxyglucose appears to be more sensitive and accurate for detecting the presence of oral and maxillofacial tumors, and has various clinical applications such as early diagnosis of tumor in initial and follow up check and detection of distant metastasis.

• Journal of Korean Orthopaedic Sports Medicine
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• v.5 no.2
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• pp.161-164
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• 2006

Purpose: Meniscal injuries are very common sports problems and indications for knee surgery. We analyzed the effectiveness of joint line tenderness retrospectively. Materials and Methods: From May 2005 to June 2006, 76 knees which were diagnosed meniscal injury and performed arthroscopic surgery by same surgeon at military hospital were included. We analyzed sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of joint line tenderness in meniscal diagnosis. Results: The joint line tenderness gave such results (78.8%, 32.6%, 52.6%, 47.3%, 66.7% for sensitivity, specificity, accuracy, positive predictive value, negative predictive value respectively). We got similar results in analyses with medial meniscal lesion, lateral meniscal lesion, and combined instability patients. Conclusion: The joint line tenderness is a easy and comfortable maneuver but, it's effectiveness is low when it is used lonely The composite examinations including MRI and diagnostic arthroscopy for meniscal injuries of the knee perform much better than joint line tenderness alone.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.87-98
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• 2022

In recent years, frequent localized heavy rainfalls, which have a lot of rainfall in a short period of time, have been increasingly causing flooding damages. To prevent damage caused by localized heavy rainfalls, Hydrological Quantitative Precipitation Forecast (HQPF) was developed using the Local ENsemble prediction System (LENS) provided by the Korea Meteorological Administration (KMA) and Machine Learning and Probability Matching (PM) techniques using Digital forecast data. HQPF is produced as information on the impact of heavy rainfall to prepare for flooding damage caused by localized heavy rainfalls, but there is a tendency to overestimate the low rainfall intensity. In this study, we improved HQPF by expanding the period of machine learning data, analyzing ensemble techniques, and changing the process of Probability Matching (PM) techniques to improve predictive accuracy and over-predictive propensity of HQPF. In order to evaluate the predictive performance of the improved HQPF, we performed the predictive performance verification on heavy rainfall cases caused by the Changma front from August 27, 2021 to September 3, 2021. We found that the improved HQPF showed a significantly improved prediction accuracy for rainfall below 10 mm, as well as the over-prediction tendency, such as predicting the likelihood of occurrence and rainfall area similar to observation.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Journal of Gastric Cancer
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• v.12 no.4
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• pp.223-231
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• 2012

Purpose: Clinical staging of gastric cancer appears to be important more and more for tailored therapy. This study aimed to verify the accuracy of clinical T staging in a low-volume institute. Materials and Methods: We retrospectively reviewed prospectively collected data of gastric cancer patients who underwent resection. A total of 268 patients of gastric cancer were enrolled from March 2004 to June 2012. These demographics, tumor characteristics, and clinical stages were analyzed for identification of diagnostic value of clinical T staging. Results: The predictive values for pT1 of endoscopy and computed tomography were 90.0% and 89.4%, respectively. In detail, the predictive values of endoscopy for pT1a, pT1b, and pT2 or more were 87%, 58.5%, and 90.6%, respectively. The predictive values of computed tomography for pT1a, pT1b, and pT2 or more were 68.8%, 73.9%, and 84.4%, respectively. The factors leading to underestimation of pT2 or more lesions by gastroscopy were the middle third location, the size greater than 2 cm, and younger age. Those for overestimation of pT1 lesion by computed tomography were male, age more than 70 years, elevated type, and size greater than 3 cm. Conclusions: Diagnostic accuracy of early gastric cancer was 90%, which is comparable to those of high volume center. In patients with early gastric cancer, limited gastrectomy or minimal invasive surgery can be safely introduced at a low volume center also. However, the surgeon of low-volume institute should consider the accuracy of clinical staging before extending the indication of limited treatment.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.4143-4147
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• 2016

Purpose: We evaluated all PET/CTs acquired for patients without a primary diagnosis of colorectal cancer, and compared results for those who had subsequent colonoscopy within 6 months, to assess the accuracy of FDG PET/CT for detection of incidental pre-malignant polyps and malignant colon cancers. Materials and Methods: Medical records of 9,545 patients who underwent F-18 FDG PET/CT studies over 3.5 years were retrospectively reviewed. Due to pre-existing diagnosis of colorectal cancer, 818 patients were excluded. Of the remainder, 157 patients had colonoscopy within 6 months (79 males; mean age 61). We divided the colon into 4 regions and compared PET/CT results for each region with colonoscopy and histopathologic findings. True positive lesions included colorectal cancer, villous adenoma, tubulovillous adenoma, tubular adenoma and serrated hyperplastic polyp/hyperplastic polyposis. Results: Of 157 patients, 44 had incidental colonic uptake on PET/CT (28%). Of those, 25 had true positive (TP) uptake, yielding a 48% positive predictive value (PPV); 9% (4/44) were adenocarcinoma. There were 23 false positive (FP) lesions of which 4 were hyperplastic polyp, one was juvenile polyp and 7 were explained by diverticulitis. Fifty eight patients had false negative PET scans but colonoscopy revealed true pre-malignant and malignant pathology, yielding 23% sensitivity. The specificity, negiative predictive value (NPV) and accuracy were 96%, 90% and 87%, respectively. The average SUVmax values of TP, FP and FN lesions were 7.25, 6.11 and 2.76, respectively. There were no significant difference between SUVmax of TP lesions and FP lesions (p>0.95) but significantly higher than in FN lesions (p<0.001). The average size (by histopathology and colonoscopy) of TP lesions was 18.1 mm, statistically different from that of FN lesions which was 5.9 mm (p<0.001). Fifty-one percent of FN lesions were smaller than 5 mm (29/57) and 88% smaller than 10 mm (50/57). Conclusions: The high positive predictive value of incidental focal colonic FDG uptake of 48% for colonic neoplasia suggests that colonoscopy follow-up is warranted with this finding. We observed a low sensitivity of standardly acquired FDG-PET/CT for detecting small polyps, especially those less than 5 mm. Clinician and radiologists should be aware of the high PPV of focal colonic uptake reflecting pre-malignant and malignant lesions, and the need for appropriate follow up.