Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment
Article
Article Title | Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Zhang, Ji (Author), Lafta, Raid (Author), Tao, Xiaohui (Author), Li, Yan (Author), Chen, Fulong (Author), Luo, Yonglong (Author) and Zhu, Xiaodong (Author) |
Journal Title | IEEE Access |
Journal Citation | 5 (1), pp. 10674-10685 |
Number of Pages | 13 |
Year | 2017 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2017.2706318 |
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. |
Keywords | recommender systems; time series analysis; intelligent systems |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460905. Information systems development methodologies and practice | |
Byline Affiliations | Faculty of Health, Engineering and Sciences |
Anhui Normal University, China | |
Nanjing University of Information Science and Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q44w5/coupling-a-fast-fourier-transformation-with-a-machine-learning-ensemble-model-to-support-recommendations-for-heart-disease-patients-in-a-telehealth-environment
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