A fast Fourier transform-coupled machine learning-based ensemble model for disease risk prediction using a real-life dataset

Lafta, Raid and Zhang, Ji and Tao, Xiaohui and Li, Yan and Abbas, Wessam and Luo, Yonglong and Chen, Fulong and Tseng, Vincent S. (2017) A fast Fourier transform-coupled machine learning-based ensemble model for disease risk prediction using a real-life dataset. In: 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), 23-26 May 2017 , Jeju, South Korea.


The use of intelligent technologies in clinical decision making have started playing a vital role in improving the quality of patients’ life and helping in reduce cost and workload involved in their daily healthcare.
In this paper, a novel fast Fourier transform-coupled machine learning based ensemble model is adopted for advising patients 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. The weightedvote
based ensemble attempts to predict the patients condition one day in advance by analyzing medical measurements of patient for the past k days. A combination of three algorithms namely neural networks, support vector machine and Naive Bayes are utilized to make an ensemble framework. A time series telehealth data recorded from patients is used for experimentations, evaluation and validation. The Tunstall dataset were collected from May to October 2012, from industry collaborator
Tunstall. The experimental evaluation shows that the proposed model yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations and also saving the workload
for patients to conduct body tests every day. The proposed method is, therefore, a promising tool for analysis of time series data and providing appropriate recommendations to patients suffering chronic diseases
with improved prediction accuracy.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online 23 April 2017. Permanent restricted access to ArticleFirst 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: 30 Jun 2017 05:15
Last Modified: 19 Sep 2018 02:31
Uncontrolled Keywords: fast Fourier transformation; ensemble model; recommender system; heart failure; time series prediction; telehealth
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080608 Information Systems Development Methodologies
Identification Number or DOI: 10.1007/978-3-319-57454-7_51
URI: http://eprints.usq.edu.au/id/eprint/32637

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