Begashaw Getnet Bogale, Zewotir Temesgen, Fenta Haile Mekonnen
Department of Statistics, College of Science, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia.
Department of Data Science, College of Natural and Computational Science, Debre Berhan University, P.O. Box 445, Debre Berhan, Ethiopia.
BioData Min. 2025 Jan 30;18(1):11. doi: 10.1186/s13040-025-00425-0.
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.
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%.
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.
本研究采用长短期记忆全连接（LSTM-FC）神经网络来解决埃塞俄比亚儿童营养不良这一关键的公共卫生问题。通过运用这种方法，该研究旨在对儿童的营养状况进行分类，并预测不同营养不良状态随时间的转变。此分析基于从“青年生活”队列研究中提取的纵向数据，该研究在2002年至2016年期间进行了五轮调查，跟踪了1997名埃塞俄比亚儿童。本文应用了严格的数据预处理，包括处理缺失值、归一化和平衡，以确保模型的最佳性能。使用夏普利值附加解释（SHapley Additive exPlanations）进行特征选择，以识别影响营养状况预测的关键因素。在模型训练期间全面应用超参数调整以优化性能。此外，本文将LSTM-FC与现有的基线模型的性能进行比较，以证明其优越性。我们在配备GPU的系统上使用Python的TensorFlow和Keras库进行模型训练。
与用于评估儿童营养状况的基线模型相比，LSTM-FC在预测准确性和长期预测方面表现更优。该模型的分类和预测性能显示出高于93%的高准确率，对正常（N）和发育迟缓与消瘦（SW）类别有完美预测，在大多数其他营养状况下误差极小，仅在少数情况下有轻微的高估或低估。LSTM-FC模型在多个折数上表现出强大的泛化性能，具有高召回率和一致的F1分数，表明其在预测营养状况方面的稳健性。我们分析了儿童从青春期后期到成年早期过渡期间的营养状况患病率。结果显示，男性正常营养状况显著下降，从5岁时的58.3%降至25岁时的33.5%。与此同时，包括体重不足、发育迟缓和消瘦（USW）在内的严重营养不良风险从1.3%增加到9.4%。
LSTM-FC模型在对埃塞俄比亚儿童营养状况进行分类和预测方面优于基线方法。研究结果揭示了营养不良情况的严重上升，强调了紧急公共卫生干预的必要性。
