• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.79-86
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• 2019

Software dimension reduction identifies the commonality of elements and extracts important feature elements. So it reduces complexity by simplify and solves multi-collinearity problems. And it reduces redundancy by performing redundancy and noise detection. In this study, we proposed defect severity-based dimension reduction model. Proposed model is applied defect severity-based NASA dataset. And it is verified the number of dimensions in the column that affect the severity of the defect. Then it is compares and analyzes the dimensions of the data before and after reduction. In this study experiment result, the number of dimensions of PC4's dataset is 2 to 3. It was possible to reduce the dimension.

• Health Policy and Management
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• v.23 no.1
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• pp.35-43
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• 2013

Background: Diagnosis procedure combination (DPC) has recently been introduced in Korea as a demonstration project and it has aimed the improvement of accuracy in bundled payment instead of Diagnosis related group (DRG). The purpose of this study is to investigate that the model of end-stage liver disease (MELD) score as the severity classification of liver diseases is adequate for improving reimbursement of DPC. Methods: The subjects of this study were 329 patients of liver disease (Korean DRG ver. 3.2 H603) who had discharged from National Health Insurance Corporation Ilsan Hospital which is target hospital of DPC demonstration project, between January 1, 2007 and July 31, 2010. We tested the cost differences by severity classifications which were DRG severity classification and clinical severity classification-MELD score. We used a multiple regression model to find the impacts of severity on total medical cost controlling for demographic factor and characteristics of medical services. The within group homogeneity of cost were measured by calculating the coefficient of variation and extremal quotient. Results: This study investigates the relationship between medical costs and other variables especially severity classifications of liver disease. Length of stay has strong effect on medical costs and other characteristics of patients or episode also effect on medical costs. MELD score for severity classification explained the variation of costs more than DRG severity classification. Conclusion: The accuracy of DRG based payment might be improved by using various clinical data collected by clinical situations but it should have objectivity with considering availability. Adequate compensation for severity should be considered mainly in DRG based payment. Disease specific severity classification would be an alternative like MELD score for liver diseases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2483-2491
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• 2013

In order to identify and understand the crucial factors to induce traffic accident, causal relationships between diverse factors and traffic accident occurrence have been investigated continuously. It is one of most important issues all over the world to reduce the number of traffic accidents and deaths by them. Korea government is also stepping up their effort to reduce the number of traffic accidents and mitigate the severity of the accidents by establishing various traffic safety strategies. By introducing the five-day work week and increasing concern of leisure activities, the differences of trip characteristics between weekday and weekend is getting greater. According to this, the patterns and crucial factors of traffic accident occurrence in weekend appear differently from those in weekday. This study aims to understand major different factors affecting accident severity between weekday and weekend using 12,042 incident data occurred on freeways of Korea from 2006 to 2011. The model developed in this study estimated relationships among various exogenous factors of traffic accident by each type using SEM(Structural Equation Model). The result provides that road factors are related to the accident severity for weekday model, while environment factors affects on accident severity for weekend.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.372-383
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• 2011

Our study was carried out to develop the severity-adjustment model for length of stay in hospital for percutaneous coronary interventions so that we would analysis the factors on the variation in length of stay(LOS). The subjects were 1,011 percutaneous coronary interventions inpatients of the Korean National Hospital Discharge In-depth Injury Survey 2004-2006 data. The data were analyzed using t-test and ANOVA and the severity-adjustment model was developed using data mining technique. After yielding the standardized value of the difference between crude and expected length of stay, we analysed the variation of length of stay for percutaneous coronary interventions. There was variation of LOS in regional differences, size of sickbed and insurance type. The variation of length of stay controlling the case mix or severity of illness can be explained the factors of provider. This supply factors in LOS variations should be more studied for individual practice style or patient management practices and healthcare resources or environment. We expect that the severity-adjustment model using administrative databases should be more adapted in other diseases in practical.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.841-851
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• 2019

This study presents risk ranking by accident types at intersections, crosswalk and tunnel sections. An ordered logit model was used to estimate the accident severity of traffic accidents based on 58,868 accident records that have occurred on the Seoul and Gyeonggi-do over the period 2014-2017. The factors affecting the injury severity were identified by the estimated model first, and risk ranking was proposed according to conditions of accident occurrence using relative ratio analysis later. The analysis results showed that the injury severity dramatically depends on the location and time of the accident. The analysis results showed that the injury severity dramatically depends on the location and time of the accident. Furthermore, there are severe injury cases in terms of the injury severity despite the small number of occurrence of traffic accident, or there are severe injury cases in terms of the injury severity despite the high frequency of occurrence of traffic accident.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.8
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• pp.101-109
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• 2019

Korea's transportation paradigm is shifting from a vehicle-oriented transportation plan to a pedestrian-friendly environment that emphasizes walking safety. However, the level of pedestrian traffic accidents in Korea is still high and serious. The purpose of this study is to investigate factors affecting the severity of pedestrians traffic accidents using the multilevel logistic regression model based on 2015-2017 pedestrian accidents data provided by the Traffic Accident Analysis System(TAAS). The main results of the multilevel logistic regression model showed that 89% of pedestrian traffic accidents in Seoul were explained by individual characteristics such as drivers and pedestrians, and 11% were explained by neighborhood environmental characteristics. The results are as follows : In the individual characteristics such as pedestrians and drivers, the older the pedestrians and the drivers, the higher the traffic accident severity. The severity of traffic accidents was high when the pedestrians were female and the drivers were male. In the case of accident types, traffic accidents were more serious in the cases of heavy vehicles, inclement weather, and occurring at intersections and crosswalks. The results of the neighborhood environmental characteristics are as follows. The intersection density and the crosswalk density tended to reduce the severity of traffic accidents. On the other hand, the traffic light density and the school zones were founded to related to the higher level of traffic accident severity. This study suggests that both individual and neighborhood environmental characteristics should be considered together to prevent and reduce the severity of pedestrian traffic accidents.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.101-111
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• 2012

PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.951-960
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• 2016

Tower insulators in electric power transmission network play a crucial role in preserving the reliability of the system. Electrical utilities frequently face the problem of flashover of insulators due to pollution deposition on their surface. Several research works based on leakage current (LC) measurement has been already carried out in developing diagnostic techniques for these insulators. Since the LC signal is highly intermittent in nature, estimation of pollution severity based on LC signal measurement over a short period of time will not produce accurate results. Reports on the measurement and analysis of LC signals over a long period of time is scanty. This paper attempts to use Random Forest (RF) classifier, which produces accurate results on large data bases, to analyze the pollution severity of high voltage tower insulators. Leakage current characteristics over a long period of time were measured in the laboratory on porcelain insulator. Pollution experiments were conducted at 11 kV AC voltage. Time domain analysis and wavelet transform technique were used to extract both basic features and histogram features of the LC signal. RF model was trained and tested with a variety of LC signals measured over a lengthy period of time and it is noticed that the proposed RF model based pollution severity classifier is efficient and will be helpful to electrical utilities for real time implementation.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.413-422
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• 2012

In this study, we developed the severity-adjusted length of stay (LOS) model for acute myocardial infarction patients using data from the hospital discharge survey and proposed management of medical quality and development of policy. The dataset was taken from 2,309 database of the hospital discharge survey from 2004 to 2006. The severity-adjusted LOS model for the acute myocardial infarction (AMI) patients was developed by data mining analysis. From decision making tree model, the main reasons for LOS of AMI patients were CABG and comorbidity. The difference between severity-adjusted LOS from the ensemble model and real LOS was compared and it was confirmed that insurance type and location of hospital were statistically associated with LOS. And to conclude, hospitals should develop the severity-adjusted LOS model for frequent diseases to manage LOS variations efficiently and apply it into the medical information system.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1123-1130
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• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.