• Health Policy and Management
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• v.28 no.3
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• pp.263-271
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• 2018

Health technology assessment (HTA) is defined as multidisciplinary policy analysis to look into the medical, economic, social, and ethical implications of the development, distribution, and use of health technology. Following the recent changes in the social environment, there are increasing needs to improve Korea's healthcare environment by, inter alia, assessing health technologies in an organized, timely manner in accordance with the government's strategies to ensure that citizens' medical expenses are kept at a stable level. Dedicated to HTA and research, the National Evidence-based Healthcare Collaborating Agency (NECA) analyzes and provides grounds on the clinical safety, efficacy, and economic feasibility of health technologies. HTA offers the most suitable grounds for decision making not only by healthcare professionals but also by policy makers and citizens as seen in a case in 2009 where research revealed that glucosamine lacked preventive and treatment effects for osteoarthritis and glucosamine was subsequently excluded from the National Health Insurance's benefit list to stop the insurance scheme from suffering financial losses and citizens from paying unnecessary medical expenses. For the development of HTA in Korea, the NECA will continue exerting itself to accomplish its mission of providing policy support by health technology reassessment, promoting the establishment and use of big data and HTA platforms for public interest, and developing a new value-based HTA system.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.71-78
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• 2019

This study aims to identify good borrowers within the context of P2P lending. P2P lending is a growing platform that allows individuals to lend and borrow money from each other. Inherent in any loans is credit risk of borrowers and needs to be considered before any lending. Specifically in the context of P2P lending, traditional models fall short and thus this study aimed to rectify this as well as explore the problem of class imbalances seen within credit risk data sets. This study implemented an over-sampling technique known as Synthetic Minority Over-sampling Technique (SMOTE). To test our approach, we implemented five benchmarking classifiers such as support vector machines, logistic regression, k-nearest neighbor, random forest, and deep neural network. The data sample used was retrieved from the publicly available LendingClub dataset. The proposed SMOTE revealed significantly improved results in comparison with the benchmarking classifiers. These results should help actors engaged within P2P lending to make better informed decisions when selecting potential borrowers eliminating the higher risks present in P2P lending.