Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare

Xie, R, Khalil, I, Badsha, S and Atiquzzaman, M 2019, 'Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare', Computer Networks, vol. 149, pp. 127-143.

Document type: Journal Article
Collection: Journal Articles

Title Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare
Author(s) Xie, R
Khalil, I
Badsha, S
Atiquzzaman, M
Year 2019
Journal name Computer Networks
Volume number 149
Start page 127
End page 143
Total pages 17
Publisher Elsevier BV * North-Holland
Abstract In modern e-healthcare systems, medical institutions can provide more reliable diagnoses by introducing Machine-Learning (ML)-based classifiers. These ML classifiers are frequently trained with huge numbers of patients data to keep updated with new diseases and changes in current disease patterns. To increase the accuracy in prediction process, Peer-to-Peer (P2P) learning systems have been explored by many stud- ies by which medical institutions can share their data with others: the more data are available, the more accurate the predictions. However, the traditional P2P network system requires much time in which the training data are shared among the nodes in the network. The system also spends much time on learning from samples where the data labels are unknown. Moreover, some nodes may perform certain compu- tations which had already been computed by other nodes, resulting in redundant computations. In this paper, in order to deal with samples having unknown data labels, we propose a Collaborative Extreme Learning Machine (CELM) with a Confidence Interval (CI), which is an enhanced version of the traditional Extreme Learning Machine (ELM). Our proposed model eliminates redundant calculations of the network nodes (the e-healthcare institutions) to improve the learning efficiency, and improves the prediction ac- curacy by considering where plausible predictions lie. The extensive experimental analysis shows that the proposed model is efficient and achieves high accuracy (up to 98%) in diagnosing clinical events by analyzing patients medical records.
Subject Pattern Recognition and Data Mining
Keyword(s) P2P network
Collaborative ELM
P2P Learning
Incremental learning
Confidence interval
Smart healthcare
DOI - identifier 10.1016/j.comnet.2018.11.002
Copyright notice © 2018 Elsevier B.V.
ISSN 1389-1286
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