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 | 
|---|---|
| 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 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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