• Journal of the Ergonomics Society of Korea
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• v.33 no.4
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• pp.289-297
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• 2014

Objective: The purpose of this study is to identify the incidence risk of cardiovascular disease (CVD) in the workplace, and to suggest the prediction models for level of CVD incidence risk. Background: CVD can be caused by various factors related to personal habits such as diet and exercise, or genetics. However it can also be caused and aggravated by work, making the elimination of such risk factors at work crucial disease (KOSHA, 2013). Method: The distribution of CVD risk assessment levels of 162 workers was compared with the acquired medical examination data to discuss the necessity of assigning additional risk factors. Two alternative risk assessment models were given to enhance the accuracy of the evaluation; adjusting risk scores given in the KOSHA GUIDE H-1-2013 (alternative 1) and building a matrix of KOSHA GUIDE H-1-2013 and risk assessment results based on work condition levels (alternative 2). To verify the suggested models, medical examination results of 12 workers approved of convalescence were referred to. Results: The second alternative showed more relevance between the results and workers approved of convalescence in predicting the risk group when applied to actual heath examination data from the approved workers. The power of description of the new method for determining the risk of CVD incidence, 83.3%, is higher than that of KOSHA GUIDE H-1-2013, 25%. Conclusion: Results of this study imply that more approved workers had been from unmanaged normal groups than managed risk groups, raising the importance of CVD management. Application: The new prediction model considering working time and shift work developed in this study is expected to be a fundamental data for risk analysis and management of CVD in the workplace.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.870-882
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• 2020

It is that failure of equipment at the factory site causes personal injury and property damage. We are required a real-time monitoring and risk forecasting techniques to prevent for equipment failure. In this paper, we proposed a 3-phase smart plug and real-time monitoring system that can be used in factories, and collected environmental information and power information using a smart plug to analyze the data. In order to analyze the correlation between the risk situation and the collected data, we predicted the risk situation using Linear Regression, SVM, and ANN algorithms. As a result, the SVM and ANN algorithms obtained high predictive accuracy and developed a mobile app that could use it to check the risk forecast results.

• Physical Therapy Korea
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• v.28 no.3
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• pp.177-185
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• 2021

Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models. Methods: This machine-learning study used the 2008-2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.185-192
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• 2022

This study investigates the effect of the semi-supervised learning(SSL) method on predicting default risk of peer-to-peer(P2P) loans. Despite its proven performance, the supervised learning(SL) method requires labeled data, which may require a lot of effort and resources to collect. With the rapid growth of P2P platforms, the number of loans issued annually that have no clear final resolution is continuously increasing leading to abundance in unlabeled data. The research data of P2P loans used in this study were collected on the LendingClub platform. This is why an SSL model is needed to predict the default risk by using not only information from labeled loans(fully paid or defaulted) but also information from unlabeled loans. The results showed that in terms of default risk prediction and despite the use of a small number of labeled data, the SSL method achieved a much better default risk prediction performance than the SL method trained using a much larger set of labeled data.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.645-648
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• 2008

This study provides considerations for quantitative risk assessment of landslide on GIS technology. It shows how the landslide possibility analysis is linked by GIS modeling to provide loss estimation tools for landslide hazards in support of socio-economic loss reduction efforts. Those risk assessment results can deliver factual damage situation prediction to policy making for the landslide damage mitigation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.6
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• pp.56-64
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• 2016

Damage of sea-crossing bridge by ship collision is related to estimate frequencies of overloading due to impact, and bridge accordingly must be designed to satisfy related acceptance criteria. Another important aspect is the management on increment of collision risk during the service period. In this study, related plan, main span length, air draft clearance and collision risk are analyzed for the interim assessment of Incheon Bridge focusing on the ship collision problem. In particular, for the increment of collision risk, the optimized navigation speed is proposed by reviewing the research findings and navigation guidelines etc. as a temporary expedient. Also basic procedure for reasonable prediction of target vessel and passage is established and probabilistic prediction method to embrace the uncertainty of the prediction is proposed as a fundamental solution. It is necessary to conduct further research on collision risk management and promptly carry out interim assessments of other marine bridges.

• Fire Science and Engineering
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• v.14 no.4
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• pp.7-16
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• 2000

In the study, three criteria(toxicity, fire & explosion, environment) and damage prediction method for each case was set up, and all these criteria were applied to the subject substance that was selected as hazardous level by integrating all criteria through Algorithm. Particularly, the environment criterion is a comprehensive concept, environment index modeling by combining USCG(United State Coast Guard) & MSDS(Material Safety Data Sheet) environment criteria classifications and the environment part of MFPA's health hazardousnes(Nh). And for damage prediction method of each criterion were adopted and they were applied to hazardous chemical substances in use or stored by chemical substance related enterprises located in each region that made possible to set up total hazard level of used substances(inflammability, poisonousness and counteraction on a unit substance, and hazard level & display modeling on environment) & damage prediction in case of accident & solidity setup(CPQRA: Chemical Process Quantitative Risk Assessment, IAEA: International Atomic Energy Agency, VZ eq: Vulnerable Zone) risk counter. Thus it is deemed that it can be applied to toxic substance leakage that can happen during any chemical processing & storage, application as a tool for prior safety evaluation through potential dangerousness computation of fire & explosion.

• Convergence Security Journal
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• v.14 no.6_2
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• pp.89-95
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• 2014

This study analyzed a correlation between crime and meteorological changes and discomfort index of Seoul and p resented a prediction expression through the regression analysis. For data used in this study, crime data from Januar y 2008 to December 2012 of Seoul Metropolitan Police Agency and meteorological records and discomfort index recor ded in the Meteorological Agency through the portal sites were used. Based on this data, SPSS 18.0 was used for the regression analysis and the analysis of correlation between crime and meteorological changes and discomfort index and a prediction expression was derived through the analysis and the risk index was shown in 5 steps depending on predicted values obtained through the prediction expression derived. The risk index of 5 steps classified like this is considered to be used as important data for crime prevention activities.

• Korean Management Science Review
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• v.33 no.3
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• pp.105-117
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• 2016

In this paper, we present empirical testing result to examine the validity of inbound supply and outbound demand risk factors in the sense of early predicting the firm's bankruptcy risk level. The risk factors are drawn from industry uncertainty attributes categorized as uncertainties of input market (inbound supply), and product market (outbound demand). On the basis of input-output table, industry level inbound and outbound sectors are identified to formalize supply chain structures, relevant inbound and outbound uncertainty attributes and corresponding risk factors. Subsequently, publicly available macro-economic indicators are used to appropriately quantify these risk factors. Total 68 industry level bankruptcy risk forecasting results are presented with the average R-square scores of between 53.4% and 37.1% with varying time lag. The findings offers useful insights to incorporate supply chain risk to the body of firm's bankruptcy risk level prediction literature.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.595-598
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• 2013

Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving. Materials and Methods: Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm. Results: Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer. Conclusions: Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.