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Trend Analysis of Fraudulent Claims by Long Term Care Institutions for the Elderly using Text Mining and BIGKinds

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2022, 8(2), pp.13-24
  • DOI : 10.20465/KIOTS.2022.8.2.013
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : February 21, 2022
  • Accepted : March 26, 2022
  • Published : April 30, 2022

Youn Ki Hyok 1

1동명대학교

Accredited

ABSTRACT

In order to explore the context of fraudulent claims and the measures for preventing them targeting the long-term care institutions for the elderly, which is increasing every year in Korea, this study conducted the text mining analysis using the media report articles. The media report articles were collected from the news big data analysis system called ‘BIG KINDS’ for about 15 years from July 2008 when the Long-Term Care Insurance for the Elderly took effect, to February 28th 2022. During this period of time, total 2,627 articles were collected under keywords like ‘elderly care+fraudulent claims󰡑 and 󰡐long-term care+fraudulent claims󰡑, and among them, total 946 articles were selected after excluding overlapped articles. In the results of the text mining analysis in this study, first, the top 10 keywords mentioned in the highest frequency in every section(July 1st 2008-February 28th 2022) were shown in the order of long-term care institution for the elderly, fraudulent claims, National Health Insurance Service, Long-Term Care Insurance for the Elderly, long-term care benefits(expenses), elderly care facilities, The Ministry of Health & Welfare, the elderly, report, and reward(payment). Second, in the results of the N-gram analysis, they were shown in the order of long-term care benefits(expenses) and fraudulent claims, fraudulent claims and long-care institution for the elderly, falsehood and fraudulent claims, report and reward(payment), and long-term care institution for the elderly and report. Third, the analysis of TF-IDF was similar to the results of the frequency analysis while the rankings of report, reward(payment), and increase moved up. Based on such results of the analysis above, this study presented the future direction for the prevention of fraudulent claims of long-term care institutions for the elderly.

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This paper was written with support from the National Research Foundation of Korea.
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