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...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s13040-025-00425-0 |
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| author | Getnet Bogale Begashaw Temesgen Zewotir Haile Mekonnen Fenta |
| author_facet | Getnet Bogale Begashaw Temesgen Zewotir Haile Mekonnen Fenta |
| author_sort | Getnet Bogale Begashaw |
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| description | 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 thoroughly applied during model training to optimize performance. Furthermore, this paper compares the performance of LSTM-FC with existing baseline models to demonstrate its superiority. We used Python's TensorFlow and Keras libraries on a GPU-equipped system for model training. Results LSTM-FC demonstrated superior predictive accuracy and long-term forecasting compared to baseline models for assessing child nutritional status. The classification and prediction performance of the model showed high accuracy rates above 93%, with perfect predictions for Normal (N) and Stunted & Wasted (SW) categories, minimal errors in most other nutritional statuses, and slight over- or underestimations in a few instances. The LSTM-FC model demonstrates strong generalization performance across multiple folds, with high recall and consistent F1-scores, indicating its robustness in predicting nutritional status. We analyzed the prevalence of children’s nutritional status during their transition from late adolescence to early adulthood. The results show a notable decline in normal nutritional status among males, decreasing from 58.3% at age 5 to 33.5% by age 25. At the same time, the risk of severe undernutrition, including conditions of being underweight, stunted, and wasted (USW), increased from 1.3% to 9.4%. Conclusions The LSTM-FC model outperforms baseline methods in classifying and predicting Ethiopian children's nutritional statuses. The findings reveal a critical rise in undernutrition, emphasizing the need for urgent public health interventions. |
| format | Article |
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| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-13040be63bd449249d5ea641970a05fc2025-02-02T12:11:40ZengBMCBioData Mining1756-03812025-01-0118112410.1186/s13040-025-00425-0A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networksGetnet Bogale Begashaw0Temesgen Zewotir1Haile Mekonnen Fenta2Department of Statistics, College of Science, Bahir Dar UniversitySchool of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-NatalDepartment of Statistics, College of Science, Bahir Dar UniversityAbstract 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 thoroughly applied during model training to optimize performance. Furthermore, this paper compares the performance of LSTM-FC with existing baseline models to demonstrate its superiority. We used Python's TensorFlow and Keras libraries on a GPU-equipped system for model training. Results LSTM-FC demonstrated superior predictive accuracy and long-term forecasting compared to baseline models for assessing child nutritional status. The classification and prediction performance of the model showed high accuracy rates above 93%, with perfect predictions for Normal (N) and Stunted & Wasted (SW) categories, minimal errors in most other nutritional statuses, and slight over- or underestimations in a few instances. The LSTM-FC model demonstrates strong generalization performance across multiple folds, with high recall and consistent F1-scores, indicating its robustness in predicting nutritional status. We analyzed the prevalence of children’s nutritional status during their transition from late adolescence to early adulthood. The results show a notable decline in normal nutritional status among males, decreasing from 58.3% at age 5 to 33.5% by age 25. At the same time, the risk of severe undernutrition, including conditions of being underweight, stunted, and wasted (USW), increased from 1.3% to 9.4%. Conclusions The LSTM-FC model outperforms baseline methods in classifying and predicting Ethiopian children's nutritional statuses. The findings reveal a critical rise in undernutrition, emphasizing the need for urgent public health interventions.https://doi.org/10.1186/s13040-025-00425-0LSTM-FCClassificationFeature selectionPredictionYoung lives cohort study |
| spellingShingle | Getnet Bogale Begashaw Temesgen Zewotir Haile Mekonnen Fenta A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks BioData Mining LSTM-FC Classification Feature selection Prediction Young lives cohort study |
| title | A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks |
| title_full | A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks |
| title_fullStr | A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks |
| title_full_unstemmed | A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks |
| title_short | A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks |
| title_sort | deep learning approach for classifying and predicting children s nutritional status in ethiopia using lstm fc neural networks |
| topic | LSTM-FC Classification Feature selection Prediction Young lives cohort study |
| url | https://doi.org/10.1186/s13040-025-00425-0 |
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