Cloud-based Ontology Context Mining using Deep Learning in Healthcare
Ji-Won Baek1, Kyungyong Chung2, Jonghun Kim3, Hoill Jung4
1Ji-Won Baek, Department of Computer Science, Kyonggi University, Gwanggyosan-Ro, Yeongtong-Gu, Suwon-Si, Gyeonggi-Do, South Korea, East Asian.
2Kyungyong Chung, Division of Computer Science and Engineering, Kyonggi University, Gwanggyosan-Ro, Yeongtong-Gu, Suwon-Si, Gyeonggi-Do, South Korea, East Asian.
3Jonghun Kim, Department of Software Convergence Engineering, Inha University, Inha‑Ro, Michuhol‑Gu, Incheon, South Korea, East Asian.
4Hoill Jung, Department of Computer Software, Daelim University College, Imgok-Ro, Dongan-Gu, Anyang-Si Gyeonggi-Do, South Korea, East Asian.
Manuscript received on 20 June 2019 | Revised Manuscript received on 27 June 2019 | Manuscript Published on 22 June 2019 | PP: 296-300 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H10540688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the development of information technologies, IT convergence technologies are being utilized in various fields. Human-oriented contents are continuously being developed to enjoy higher quality life through IT convergence technologies. Health care services resulting from the development of various smart IT devices in the health and medical field make more efficient health management possible for people. This study proposes a cloud-based ontology context mining method using deep-learning in health care. The user’s static data and context data are saved in cloud, which is a high performance computing resource through ontology modeling and context mining is used to collect user health data with high similarity. The collected health data are applied to the algorithms and back propagation of artificial neural networks and deep-learning is conducted to provide more accurate health care prediction service. Furthermore, upon conducting a performance analysis to verify the validity of the learned user health data, it was found that the prediction accuracy that applied the proposed method was approximately 17% higher.
Keywords: Cloud Computing, Deep Learning, Healthcare, Ontology, Context Mining
Scope of the Article: Deep Learning