A recommender system with advanced time series medical data analysis for diabetes patients in a telehealth environment

Lafta, Raid and Zhang, Ji ORCID: https://orcid.org/0000-0001-7167-6970 and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Lin, Jerry Chun-Wei and Chen, Fulong and Luo, Yonglong and Zheng, Xiaoyao (2018) A recommender system with advanced time series medical data analysis for diabetes patients in a telehealth environment. In: 29th International Conference on Database and Expert Systems Applications (DEXA 2018), 3-6 Sept 2018, Regensburg, Germany.


Intelligent technologies are enjoying growing popularity in a telehealth environment for helping improve the quality of chronic patients’ lives and provide better clinical decision-making to reduce the costs and workload involved in their daily healthcare. Obtaining a short-term disease risk prediction and thereby offering medical recommendations reliably and accurately are challenging in teleheath systems. In this work, a novel medical recommender system is proposed based upon time series data analysis for diabetes patients. It uses three decomposition methods, i.e., dual-tree complex wavelet transform (DTCWT), fast Fourier transformation (FFT) and dual-tree complex wavelet transform-coupled fast Fourier transform (DWCWT-FFT), with least square-support vector machine (LS-SVM) for short-term disease risk prediction for diabetes disease patients which then generates appropriate recommendations on their need to take a medical test or not on the coming day based on the analysis of their medical data. A real-life time series dataset is used for experimental evaluation. The experimental results show that the proposed system yields very good recommendation accuracy and can effectively reduce the workload for diabetes disease patients in conducting daily body tests.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 - 31 Mar 2020)
Date Deposited: 22 May 2020 06:04
Last Modified: 19 Dec 2021 22:01
Uncontrolled Keywords: decomposition methods, recommender system, diabetes disease patients, time series prediction, telehealth, SVM
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/978-3-319-98812-2_15
URI: http://eprints.usq.edu.au/id/eprint/36150

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