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Assessment of Dissaving Risk against Life Expectancy for Elderly People through Anonymous Data and Random Data

  • Yuya Yokoyama
  • , Yasunari Yoshitomi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In order to detect the capability deterioration of economic activity for elderly people at age of sixty-five or over, we have used an anonymous data obtained from the National Survey of Family Income and Expenditure (NSFIE) carried out by Ministry of Internal Affairs and Communications (MIAC). We develop a method to detect dissaving risk of elderly people. So far the analysis data were divided into test data and training data. Then three kinds of methods were performed in terms of income and savings. Two-step methods were taken to determine dissaving risk. In using anonymous data, however, there is controversy if anonymity is secured. In order to strengthen the anonymity of the data, in this paper, random data is generated with using the anonymous data and then compared with the case of analyzing anonymous data as it is, with a view to performance evaluation. As a result of analysis, it could be concluded that using the random data would be as effective as using anonymous data for evaluating the performance of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019
EditorsMasahide Nakamura, Hiroaki Hirata, Takayuki Ito, Takanobu Otsuka, Shun Okuhara
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages274-279
Number of pages6
ISBN (Electronic)9781728116518
DOIs
Publication statusPublished - 07-2019
Externally publishedYes
Event20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019 - Toyama, Japan
Duration: 08-07-201911-07-2019

Publication series

NameProceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019

Conference

Conference20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019
Country/TerritoryJapan
CityToyama
Period08-07-1911-07-19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software
  • Information Systems and Management

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