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

  • Kyungyong Chung
  • Hoill JungEmail author


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.


Healthcare management Dynamic cluster model CNN Knowledge discovery 



This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. 2018R1C1B5047242).


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Computer Science and EngineeringKyonggi UniversitySuwon-siSouth Korea
  2. 2.Department of Computer SoftwareDaelim University CollegeAnyang-siSouth Korea

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