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

Authors of the publication

Number of authors
3
Authors
Begashaw, Getnet Bogale; Zewotir, Temesgen; Fenta, Haile Mekonnen

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

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