Hyperparameter-Tuned Artificial Neural Networks for Early Stunting Prediction in Toddlers
DOI:
10.33395/sinkron.v9i2.14695Keywords:
Artificial Neural Networks, GridSearchCV, Hyperparameter, Optimization, Stunting PredictionAbstract
The growing accessibility of varied health data requires the creation of efficient and practical techniques for deriving actionable insights, particularly for the early identification of severe health issues. This study tackles the issue of recognizing stunting—a disorder with enduring health consequences—among children under five by employing Artificial Neural Networks (ANN) with hyperparameter optimization by GridSearchCV. The dataset, sourced from Kaggle, comprises 121,000 records detailing age, gender, height, and nutritional status according to WHO standards. Critical hyperparameters, including dropout rate, batch size, and epochs, were optimized using a five-fold cross-validation approach within GridSearchCV, ensuring a robust model that reduces overfitting and generalizes well to new data. The findings demonstrate a notable performance improvement, as the optimized ANN model attained an accuracy of 99%, exceeding the baseline model's 98%. Enhancements in accuracy, recall, and F1-score across the four stunting classifications—normal, stunted, severely stunted, and tall—underscore the improved specificity and sensitivity of the optimized model. This research demonstrates the efficacy of hyperparameter tuning in ANN for stunting prediction, offering a reliable tool for early intervention in malnutrition management.
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Copyright (c) 2025 Salnan Ratih Asriningtias, Citra Dewi Megawati, Dian Kusumaningtyas, Dwi Utari Surya
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