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Breaking Class Imbalance: Machine Learning Solutions for Stunting Detection

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K-Nearest Neighbors with SMOTE achieved the highest accuracy of 99.17% in detecting stunting, demonstrating superior performance.
Decision Tree's accuracy improved to 97.41% with SMOTE, underlining the importance of class imbalance techniques.
Random undersampling significantly reduced performance in both models, suggesting careful selection of strategies is necessary.
The findings reinforce the efficacy of machine learning in addressing public health challenges like stunting detection.

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This analysis evaluates machine learning models for detecting stunting, highlighting the role of class imbalance management techniques like SMOTE.

Raihana et al. (2025) studied this question.

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This analysis evaluates machine learning models for detecting stunting, highlighting the role of class imbalance management techniques like SMOTE.