A structural graph-coupled advanced machine learning ensemble model for disease risk prediction in a telehealthcare environment

Lafta, Raid and Zhang, Ji and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Li, Yan and Diykh, Mohammed and Lin, Jerry Chun-Wei (2018) A structural graph-coupled advanced machine learning ensemble model for disease risk prediction in a telehealthcare environment. In: Big data in engineering applications. Studies in Big Data. Springer, Singapore, pp. 363-384. ISBN 978-981-10-8475-1


Abstract

The use of intelligent and sophistic technologies in evidence-based clinical decision making support have been playing an important role in improving the quality of patients’ life and helping to reduce cost and workload involved in their daily healthcare. In this paper, an effective medical recommendation system that uses a structural graph approach with advanced 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. 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. A time series data is segmented into slide windows and then mapped into undirect graph. The size of slide window was empirically determined. The structural properties of graph enter as the features set to the machine learning ensemble classifier to predict the patient’s condition one day in advance. A combination of three classifiers—Least Squares-Support Vector Machine, Artificial Neural Network, and Naive Bayes—are used to construct an ensemble framework to classify the graph features. To investigate the predictive ability of the graph with the ensemble classifier, the extracted statistical features were also forwarded to the individual classifiers for comparison. The findings of this study shows that the recommendation system yields a satisfactory recommendation accuracy, offers a effective way for reducing the risk of incorrect recommendations as well as reducing the workload for heart disease patients in conducting body tests every day. A 94% average prediction accuracy is achieved by using the proposed recommendation system. 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.


Statistics for USQ ePrint 38119
Statistics for this ePrint Item
Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published Chapter and front Matter 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 - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 10 Jun 2020 04:54
Last Modified: 07 Oct 2020 05:38
Uncontrolled Keywords: ensemble machine learning, disease risk prediction, ensemble model, chronic heart disease patients, sliding window
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/10.1007/978-981-10-8476-8_18
URI: http://eprints.usq.edu.au/id/eprint/38119

Actions (login required)

View Item Archive Repository Staff Only