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 time. This analysis is based on longitudinal data extracted from the Young Lives cohort study, which tracked 1,997 Ethiopian children across five survey rounds conducted from 2002 to 2016. This paper applies rigorous data preprocessing, including handling missing values, normalization, and balancing, to ensure optimal model performance. Feature selection was performed using SHapley Additive exPlanations to identify key factors influencing nutritional status predictions. Hyperparameter tuning was thoro

SUBMITTER: Begashaw GB 

PROVIDER: S-EPMC11783927 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature