IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment

Lafta, Raid and Zhang, Ji and Tao, Xiaohui and Li, Yan and Tseng, Vincent S. (2016) IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment. In: 12th International Conference on Advanced Data Mining and Applications (ADMA 2016), 12-15 Dec 2016, Gold Coast, QLD, Australia.

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Abstract

The use of intelligent technologies in clinical decision making support may play a promising role in improving the quality of heart disease patients’ life and helping to reduce cost and workload involved in their daily health care in a tele-health environment. The objective of this demo proposal is to demonstrate an intelligent prediction system we developed, called IRS-HD, that accurately advises patients with heart diseases concerning whether they need to take the body test today or not based on the analysis of their medical data during the past a few days. Easy-to-use user friendly interfaces are developed for users to supply necessary inputs to the system and receive recommendations from the system. IRS-HD yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations, as well saves the workload for patients to conduct body tests every day.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 16 Feb 2017 23:52
Last Modified: 03 Jan 2018 06:48
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Identification Number or DOI: 10.1007/978-3-319-49586-6_58
URI: http://eprints.usq.edu.au/id/eprint/30359

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