ArticleSensors (Basel, Switzerland)2023
Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- A hybrid method for fusion cardiac biomarkers and echocardiography videos in the experimental classification of Trypanosoma cruzi infection.Biomedical engineering online · 2025Article
- Early prediction of low birth weight using boosting ensemble machine learning: A retrospective cohort study.Digital healthArticle
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Authors and funding
4 authors.
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Abstract
In this work, a novel multimodal learning approach for early prediction of birth weight is presented. Fetal weight is one of the most relevant indicators in the assessment of fetal health status. The aim is to predict early birth weight using multimodal maternal-fetal variables from the first trimester of gestation (Anthropometric data, as well as metrics obtained from Fetal Biometry, Doppler and Maternal Ultrasound). The proposed methodology starts with the optimal selection of a subset of multimodal features using an ensemble-based approach of feature selectors. Subsequently, the selected variables feed the nonparametric Multiple Kernel Learning regression algorithm. At this stage, a set of kernels is selected and weighted to maximize performance in birth weight prediction. The proposed methodology is validated and compared with other computational learning algorithms reported in the state of the art. The obtained results (absolute error of 234 g) suggest that the proposed methodology can be useful as a tool for the early evaluation and monitoring of fetal health status through indicators such as birth weight.
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