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 proceedingConference contributionScientificpeer-review


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
Number of pages10
Place of PublicationCham
Publication date2021
ISBN (Print)978-3-030-58988-2
ISBN (Electronic)978-3-030-58989-9
Publication statusPublished - 2021
MoE publication typeA4 Article in conference proceedings

Publication series

NameProceedings in Adaptation, Learning and Optimization
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092


  • 113 Computer and information sciences


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