Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1289
Title: Effectiveness of artificial intelligence, decentralized and distributed systems for prediction and secure channelling for Medical Tourism
Authors: Subasinghe, M
Magalage, D
Amadoru, N
Amarathunga, L
Bhanupriya, N
Wijekoon, J
Keywords: Effectiveness
artificial intelligence
decentralized
distributed systems
prediction
secure channelling
Medical Tourism
Issue Date: 4-Nov-2020
Publisher: IEEE
Citation: M. Subasinghe, D. Magalage, N. Amadoru, L. Amarathunga, N. Bhanupriya and J. L. Wijekoon, "Effectiveness of artificial intelligence, decentralized and distributed systems for prediction and secure channelling for Medical Tourism," 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2020, pp. 0314-0319, doi: 10.1109/IEMCON51383.2020.9284898.
Series/Report no.: 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON);Pages 0314-0319
Abstract: Good health and wellbeing, a sustainable development goal introduced by the United Nations to be achieved by 2030. Sri Lanka is a country that highly depends on tourism. A healthcare system which consists of high quality and low-cost services and an abundance of tourist attractions makes Sri Lanka to be one of the best medical tourism destinations. Tourism and travel have contributed to the GDP of Sri Lanka by 11.1 billion USD by 2018. Lack of technological advancements within the medical sector has drawn back the ability to smoothly cater medical tourism. The proposed system aims for an advanced technological improvement that would help in further developing and contributing to medical tourism. To this end, this paper introduces an Intelligent System for Secure Channeling platform that aids medical tourism with the help of artificial intelligence and blockchain technologies. System proposes a treatment prediction and suggesting the best doctor for it and a secured network to store and access electronic health records (EHR). The yielded results show that the proposed method successfully performed treatment prediction with 79-88% accuracy.
URI: http://rda.sliit.lk/handle/123456789/1289
ISSN: 2644-3163
Appears in Collections:Department of Computer Systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications



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