Abstract
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 weighted-vote 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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Acknowledgement
The authors would like to thank the support from National Science Foundation of China through the research projects (Nos. 61572036, 61370050, and 61672039) and Guangxi Key Laboratory of Trusted Software (No. kx201615).
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Lafta, R. et al. (2017). A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_51
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