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Yang, Y, Nan, F, Yang, P, Meng, Q, Xie, Y, Zhang, D and Muhammad, K (2019) GAN-Based Semi-Supervised Learning Approach for Clinical Decision Support in Health-IoT Platform. IEEE Access, 7. ISSN 2169-3536
Full text not available from this repository. Please see publisher or open access link below:Abstract
With the development of the Internet of Things (IoT) technology, its application in the medical field becomes more and more extensive. However, with a dramatic increase in medical data obtained from the IoT-based health service system, labeling a large number of medical data requires high cost and relevant domain knowledge. Therefore, how to use a small number of labeled medical data reasonably to build an efficient and high-quality clinical decision support model in the IoT-based platform has been an urgent research topic. In this paper, we propose a novel semi-supervised learning approach in association with generative adversarial networks (GANs) for supporting clinical decision making in the IoT-based health service system. In our approach, GAN is adopted to not only increase the number of labeled data but also to compensate the imbalanced labeled classes with additional artificial data in order to improve the semi-supervised learning performance. Extensive evaluations on a collection of benchmarks and real-world medical datasets show that the proposed technique outperforms the others and provides a potential solution for practical applications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Science & Technology; Technology; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications; Computer Science; Engineering; Internet of Things; clinical decision support; semi-supervised learning; generative adversarial networks; CLUSTERING ENSEMBLE; FEATURE-SELECTION; INTERNET; THINGS; SYSTEMS; CARE |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Related URLs: | |
| Date Deposited: | 26 Feb 2019 09:41 |
| Last Modified: | 03 Sep 2021 23:41 |
| DOI or ID number: | 10.1109/ACCESS.2018.2888816 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/10213 |
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