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Research Articles

Low Birth Weight Classification With Synthetic Minority Over Sampling Technique Random Forest

Sachnaz Desta Oktarina IPB University, Indonesia Correspondence sachnazdes@apps.ipb.ac.id
,
Hari Wijayanto 3Dept. of Statistics and Data Science, IPB University, Bogor, Indonesia, Indonesia Correspondence hari@apps.ipb.ac.id
,
Helena Ramadhini Yarah Dept. of Statistics and Data Science, IPB University, Bogor, Indonesia, Indonesia Correspondence helena_yarah@apps.ipb.ac.id
Pages 46 - 56
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Abstract

Low birth weight (LBW) is defined as a condition where the birth weight is less than 2500 grams. Infants born with LBW conditions are more susceptible to disease and have a higher risk of dying at an early age. LBW conditions that are prone to unbalanced data can be classified using the Synthetic Minority Oversampling Technique (SMOTE) random forest method. The analysis was processed on the 2017 Indonesian Demographic and Health Survey (IDHS) data to identify important variables in predicting the incidence of LBW. The results showed that the SMOTE random forest model provided an accuracy value of 79.84%, sensitivity of 30.99%, specificity of 83.6%, and AUC of 62%. Important variables in predicting the incidence of LBW were the number of antenatal care visits, wealth quantile, maternal age at delivery, iron supplementation, marital status, and twins’ birth.

Keywords :

LBW SMOTE random forest

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How to Cite This

Oktarina, S. D., Wijayanto, H., & Yarah, H. R. . (2023). Low Birth Weight Classification With Synthetic Minority Over Sampling Technique Random Forest. Jurnal Kesehatan Ibu Dan Anak, 17(1), 46–56. https://doi.org/10.29238/kia.v17i1.1802 (Original work published October 31, 2023)

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All data generated during this study are included in this published article [and its supplementary information files].