A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks
Approved
Classifications
MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal
Publication channel information
Title of journal/series
Biodata mining
ISSN (electronic)
1756-0381
ISSN (linking)
1756-0381
Publisher
Biomed central
Publication forum ID
70165
Publication forum level
1
Country of publication
United Kingdom
Internationality
Yes
Detailed publication information
Publication year
2025
Bibliographical publication year
2025
Reporting year
2025
Journal/series volume number
18
Article number
11
DOI
10.1186/s13040-025-00425-0
Language of publication
English
Co-publication information
International co-publication
No
Co-publication with a company
No
Availability
Link to online publication
Link to self-archived version
Classification and additional information
MinEdu field of science classification
3142 Public health care science, environmental and occupational health, 3111 Biomedicine, 318 Medical biotechnology
Keywords
Classification; Feature selection; LSTM-FC; Prediction; Young lives cohort study
Funding information
Funding information in the publication
This research has no specific grant from any funding agency, commercial, or not-for-profit sectors to report.
Research data information
Research data information in the publication
The dataset used in this study was obtained from the Young Lives Study. Access to the data can be obtained either by completing the form available at https://www.younglives.org.uk/use-our-data-form selecting the dataset "Young Lives: Rounds 1-5 constructed files, 2002-2016" (used in this study), or by creating a user account through the https://ukdataservice.ac.uk/ subject to their terms and conditions. Additionally, the survey questionnaires for each round (Rounds 1-5) are available through the following link: https://www.younglives.org.uk/round-1-questionnaires. By adjusting the round number in the URL or navigating through the menu on the Young Lives website, users can access the questionnaires for each respective round.
Source database ID
WoS ID
WOS:001409579800002
Scopus ID
2-s2.0-85218239215
Other database ID
PMID: 39885567