• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.73-82
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• 2022

Purpose: The purpose of this study is to evaluate the effectiveness of reducing the absorbed dose to the ovaries and the quality of the CBCT image when using the Halcyon^TM Fast kV CBCT of cervical cancer patients of child-bearing age who performed ovarian transposition Materials and Methods : Contouring of the cervix and ovaries required for measurement was performed on the computed tomography images of the human phantom (Alderson Rando Phantom, USA), and three Optically Stimulated Luminescence Dosimeter(OSLD) were attached to the selected organ cross-section, respectively. In order to measure the absorbed dose to the cervix and ovaries in the Truebeam^TM pelvis mode (Hereinafter referred to as TP), The Halcyon^TM Pelvis mode (Hereinafter referred to as HP) and The Halcyon^TM Pelvis Fast mode (Hereinafter referred to as HPF), An image was taken with a scan range of 17.5 cm and also taken an image that reduced the Scan range to 12.5cm. A total of 10 cumulative doses were summed, It was replaced with a value of 23 Fx, the number of cervical cancer treatments, and compared In additon, uniformity, low contrast visibility, spatial resolution, and geometric distortion were compared and analyzed using Catphan 504 phantom to compare CBCT image quality between equipment. Each factor was repeatedly measured three times, and the average value was obtained by analysing with the Doselab (Mobius Medical Systems, LP. Versions: 6.8) program. Results: As a result of measuring absorbed dose by CBCT with OSLD, TP and HP did not obtain significant results under the same conditions. The mode showing the greatest reduction value was HPF versus TP. In HPF, the absorbed dose was reduced by 39.8% in the cervix and 19.8% in the ovary compared to the TP in the scan range of 17.5 cm. the scan range was reduced to 12.5 cm, absorbed dose was reduced by 34.2% in the cervix and 50.5% in the ovary. In addition, result of evaluating the quality of the image used in the above experiment, it complied with the equipment manufacturer's standards with Geometric Distortion within 1mm (SBRT standard), Uniformity HU, LCV within 2.0%, Spatial Resolution more than 3 lp/mm. Conclusion: According to the results of this experiment, Halcyon^TM can select more various conditions than Truebeam^TM in treatment of fertility woman who have undergone ovarian Transposition , because it is important to reduce the radiation dose by CBCT during radiation therapy. So finally we recommend Halcyon^TM Fast kV CBCT which maintains image quality even at low mAs. However, it is consider that the additional exposure to low doses can be reduced by controlling the imaging range for patients who have undergone ovarian transposition in other treatment machines.