Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment

Zhang, Ji and Lafta, Raid and Tao, Xiaohui and Li, Yan and Chen, Fulong and Luo, Yonglong and Zhu, Xiaodong (2017) Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. IEEE Access, 5 (1). pp. 10674-10685.

Abstract

Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients' lives and helping reduce the costs and workload involved in their daily healthcare. In this paper, an effective medical recommendation system that uses a fast Fourier transformation-coupled machine learning ensemble model is proposed for short-term disease risk prediction to provide chronic heart disease patients with appropriate recommendations about the need to take a medical test or not on the coming day based on analysing their medical data. The input sequence of sliding windows based on the patient's time series data are decomposed by using the fast Fourier transformation in order to extract the frequency information. A bagging-based ensemble model is utilized to predict the patient's condition one day in advance for producing the final recommendation. A combination of three classifiers -- Artificial Neural Network, Least Squares-Support Vector Machine, and Naive Bayes -- are used to construct an ensemble framework. A real-life time series telehealth data collected from chronic heart disease patients is utilized for experimental evaluation. The experimental results show that the proposed system yields a very good recommendation accuracy and offers an effective way to reduce the risk of incorrect recommendations as well as reduce the workload for heart disease patients in conducting body tests every day. The results conclusively ascertain that the proposed system is a promising tool for analyzing time series medical data and providing accurate and reliable recommendations to patients suffering from chronic heart diseases.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, 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: 10 Nov 2017 00:16
Last Modified: 23 Apr 2018 01:59
Uncontrolled Keywords: recommender systems; time series analysis; intelligent systems
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
08 Information and Computing Sciences > 0806 Information Systems > 080608 Information Systems Development Methodologies
Identification Number or DOI: 10.1109/ACCESS.2017.2706318
URI: http://eprints.usq.edu.au/id/eprint/32633

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