A Smart Architecture for Stunting Prediction: Implementing the SOM–Voting Classifier on Healthcare Big Data

Kelvin Kelvin; Sunaryo Winardi; Frans Mikael Sinaga; Hardy Hardy; Erwin Setiawan Panjaitan; Ng Poi Wong; Ferawaty Ferawaty; Justine Lim; Grace Putri Wijaya

doi:10.24014/ijaidm.v8i3.38000

A Smart Architecture for Stunting Prediction: Implementing the SOM–Voting Classifier on Healthcare Big Data

Kelvin Kelvin, Sunaryo Winardi, Frans Mikael Sinaga, Hardy Hardy, Erwin Setiawan Panjaitan, Ng Poi Wong, Ferawaty Ferawaty, Justine Lim, Grace Putri Wijaya

Abstract

Childhood stunting is a persistent public health challenge in Indonesia. This study developed a predictive classification model using healthcare data from hospitals in Medan to enable early identification of at-risk children. A novel framework was proposed that integrated an unsupervised Self-Organizing Map (SOM) for feature engineering with a supervised Voting Classifier ensemble, which combined a Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB). The proposed framework achieved an accuracy of 100% on the test set, a substantial improvement over the 91.67% accuracy of the baseline Voting Classifier without SOM. While this result highlighted the model's high predictive potential, it must be interpreted cautiously, acknowledging the need for validation on more diverse datasets to ensure generalizability. The findings demonstrated that this hybrid machine learning approach can serve as a powerful decision-support tool, enabling proactive clinical interventions and aiding public health officials in strategically allocating nutritional resources to support Indonesia's national stunting reduction goals.

Keywords

Big Data; Medical; Stunting; Support Vector Classifier; Voting Classifier

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DOI: http://dx.doi.org/10.24014/ijaidm.v8i3.38000

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