Toddler Nutritional Status Classification For Early Detection Of Malnutrition Using Xgboost: A Case Study Of “West Lombok Regency
Keywords:
XGBoost, Machine Learning, Toddler Nutritional Status, Anthropometry, Early Detection of Malnutrition, Class ImbalanceAbstract
Malnutrition among toddlers remains a critical challenge in West Lombok Regency, with a stunting prevalence reaching 32.7% in 2022. This study aims to develop a classification system for toddlers' nutritional status using XGBoost, with class imbalance handled through SMOTE. The dataset consists of 788 toddlers aged 24–59 months from 12 villages in West Lombok District. Preprocessing steps include filtering biologically invalid values based on WHO criteria, normalization using MinMaxScaler, and feature engineering through anthropometric ratios such as weight-for-height (WHZ) and height-for-age (HAZ). The data is split using a stratified approach with an 80:20 ratio, and SMOTE is applied exclusively to the training set. Evaluation using macro F1-score and minority class recall shows that XGBoost achieves an F1-score of 94.3% and a recall of 92.1% for severe malnutrition, significantly outperforming Random Forest (89.7%), KNN (84.2%), Naïve Bayes (81.5%), and Decision Tree (83.8%). A Streamlit-based prototype was also developed as a practical interface for community health workers (posyandu cadres), featuring prediction tools, distribution visualizations, and automated referral recommendations. The results demonstrate that XGBoost combined with SMOTE is effective in improving early detection of minority malnutrition cases in imbalanced populations, supporting stunting reduction targets.
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