Lightweight Hybrid Feature Engineering and Fusion with Variational Autoencoder and XGBoost for Nutritional Data Classification

Wiwien Hadikurniawati; Kristoko Dwi Hartomo; Irwan Sembiring

doi:10.48084/etasr.15310

Authors

Wiwien Hadikurniawati Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia | Faculty of Information Technology and Industry, Universitas Stikubank, Semarang 50241, Indonesia
Kristoko Dwi Hartomo Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia
Irwan Sembiring Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia

Volume: 16 | Issue: 1 | Pages: 30859-30868 | February 2026 | https://doi.org/10.48084/etasr.15310

Received: 4 October 2025 | Revised: 25 October 2025 and 30 October 2025 | Accepted: 3 November 2025 | Online: 9 February 2026

Corresponding author: Wiwien Hadikurniawati

Abstract

Stunting remains a significant global health challenge, particularly among children under five years old. Early detection of nutritional status is crucial to prevent long-term impacts on both physical and cognitive development. This study proposes a lightweight approach based on hybrid feature engineering and fusion for stunting status classification without requiring complex data balancing strategies. The proposed method integrates basic anthropometric features with Rule-Based (RB) derived features (BMI, weight, and height changes since birth, growth rates, and z-score combinations), along with latent features generated by a Variational Autoencoder (VAE) that captures non-linear patterns in tabular data. All features are then concatenated and classified using XGBoost. The evaluation with 5-fold cross-validation demonstrated highly competitive performance, yielding an average F1-score of 0.9919 ± 0.0066 on the testing data. These findings highlight that a lightweight yet informative approach can serve as a practical solution to support early stunting detection, particularly in public health settings with limited resources.

Keywords:

stunting prediction, feature engineering, Variational Autoencoder, XGBoost, lightweight model

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