IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment
Paper
Paper/Presentation Title | IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment |
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Presentation Type | Paper |
Authors | Lafta, Raid (Author), Zhang, Ji (Author), Tao, Xiaohui (Author), Li, Yan (Author) and Tseng, Vincent S. (Author) |
Editors | Li, Jinyan, Li, Xue, Wang, Shuliang, Li, Jianxin and Sheng, Quan Z. |
Journal or Proceedings Title | Lecture Notes in Artificial Intelligence (Book series) |
ERA Conference ID | 43204 |
Journal Citation | 10086, pp. 803-806 |
Number of Pages | 4 |
Year | 2016 |
Place of Publication | Switzerland |
ISBN | 9783319495859 |
9783319495866 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-49586-6_58 |
Conference/Event | 12th International Conference on Advanced Data Mining and Applications (ADMA 2016) |
International Conference on Advanced Data Mining and Applications | |
Event Details | International Conference on Advanced Data Mining and Applications ADMA Rank B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B |
Event Details | 12th International Conference on Advanced Data Mining and Applications (ADMA 2016) Event Date 12 to end of 15 Dec 2016 Event Location Gold Coast, QLD, Australia |
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. |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
National Chiao Tung University, Taiwan | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3w24/irs-hd-an-intelligent-personalized-recommender-system-for-heart-disease-patients-in-a-tele-health-environment
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