ELM Feature Selection and SOM Data Visualization for Nursing Survey Datasets

Renjie Hu*, Amany Farag, Kaj-Mikael Björk, Amaury Lendasse

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

This paper presents a novel methodology to analyze nursing surveys. It is based on ELM and SOM. The goal is to identify which variables lead to the likelihood to report the medication errors. ELM are accurate by extremely fast prediction models. SOM are performing nonlinear dimensionality reduction to get an accurate visualization of the data. Combining both techniques reduces the curse of dimensionality and improves furthermore the interpretability of the visualization. The methodology is tested on a nursing survey datasets.
Original languageEnglish
Title of host publicationProceedings of ELM2019
EditorsJiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
PublisherSpringer
Publication date12.09.2020
Pages99-108
ISBN (Print)978-3-030-58988-2
ISBN (Electronic)978-3-030-58989-9
DOIs
Publication statusPublished - 12.09.2020
MoE publication typeA3 Book chapter

Publication series

NameProceedings of ELM2019
Volume14
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092

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  • Cite this

    Hu, R., Farag, A., Björk, K-M., & Lendasse, A. (2020). ELM Feature Selection and SOM Data Visualization for Nursing Survey Datasets. In J. Cao, C. M. Vong, Y. Miche, & A. Lendasse (Eds.), Proceedings of ELM2019 (pp. 99-108). (Proceedings of ELM2019; Vol. 14). Springer. https://doi.org/10.1007/978-3-030-58989-9_11