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Knowledge-based dynamic cluster model for healthcare management using a convolutional neural network

Author

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  • Kyungyong Chung

    (Kyonggi University)

  • Hoill Jung

    (Daelim University College)

Abstract

Due to recent growing interest, the importance of preventive and efficient healthcare using big data scattered throughout various IoT devices is being emphasized in healthcare, as well in the IT field. The analysis of information in healthcare is mainly prediction using a user’s basic information and static data from a knowledge base. In this study, a knowledge-based dynamic cluster model using a convolutional neural network (CNN) is suggested for healthcare recommendations. The suggested method carries out a process to extend static data and a previous knowledge base from an ontology-based ambient-context knowledge base beyond knowledge-based healthcare management, which was the focus of previous study. It is possible to acquire and expand a large amount of high-quality information by reproducing inferred knowledge using a CNN, which is a deep-learning algorithm. A dynamic cluster model is developed, and the accuracy of the predictions is improved in order to enable recommendations on healthcare according to a user environment that changes over time and based on environmental factors as dynamic elements, rather than static elements. Also, the accuracy of the predictions is verified through a performance evaluation between the suggested method and the previous method to validate effectiveness, and an approximate 13% performance improvement was confirmed. Currently, the acquisition of knowledge from unstructured data is in its early stages. It is expected that symbolic knowledge-acquisition technology from unstructured information that is produced and that changes in real time, and the dynamic cluster model method suggested in this study, will become the core technologies that promote the development of healthcare management technology.

Suggested Citation

  • Kyungyong Chung & Hoill Jung, 2020. "Knowledge-based dynamic cluster model for healthcare management using a convolutional neural network," Information Technology and Management, Springer, vol. 21(1), pages 41-50, March.
    • Handle: RePEc:spr:infotm:v:21:y:2020:i:1:d:10.1007_s10799-019-00304-1
    DOI: 10.1007/s10799-019-00304-1
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    References listed on IDEAS

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    1. Kyungyong Chung & Joo-Chang Kim & Roy C. Park, 2016. "Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P," Information Technology and Management, Springer, vol. 17(1), pages 67-80, March.
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    Cited by:

    1. Hector John T. Manaligod & Michael Joseph S. Diño & Sunmoon Jo & Roy C. Park, 0. "Knowledge discovery computing for management," Information Technology and Management, Springer, vol. 0, pages 1-2.
    2. Fu-Hsiang Chen & Ming-Fu Hsu & Kuang-Hua Hu, 2022. "Enterprise’s internal control for knowledge discovery in a big data environment by an integrated hybrid model," Information Technology and Management, Springer, vol. 23(3), pages 213-231, September.
    3. Hector John T. Manaligod & Michael Joseph S. Diño & Sunmoon Jo & Roy C. Park, 2020. "Knowledge discovery computing for management," Information Technology and Management, Springer, vol. 21(2), pages 61-62, June.

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