A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks

A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks

Abstract Background This study employs a LSTM-FC neural networks to address the critical public health issue of child undernutrition in Ethiopia. By employing this method, the study aims classify children's nutritional status and predict transitions between different undernutrition states over...

Full description

Saved in:
Bibliographic Details
Main Authors: Getnet Bogale Begashaw, Temesgen Zewotir, Haile Mekonnen Fenta
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BioData Mining
Subjects:
LSTM-FC
Classification
Feature selection
Prediction
Young lives cohort study
Online Access:https://doi.org/10.1186/s13040-025-00425-0
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s13040-025-00425-0

Similar Items