Multiple Output Gaussian Process Model for Predicting Low Birth Weight in Medellín, Colombia: An Alternative to Conventional Machine Learning Models.

Authors

Diego Alejandro Salazar Blandon, Hernán Felipe García Arias, Juan José Giraldo Gutiérrez

Published in

Maternal and child health journal. Jun 29, 2026. Epub Jun 29, 2026.

Abstract

To evaluate the methodological feasibility of a heterogeneous multi-output Gaussian process model for jointly handling a continuous birth outcome and its clinically used binary representation in routinely collected perinatal data, and to compare its predictive performance with that of conventional single-output models.
Routinely collected live-birth certificate data from Medellín, Colombia, covering births from 2012 to 2021, were analyzed. After cleaning and class balancing, the analytic dataset included 32,110 records. A heterogeneous multi-output Gaussian process model was trained to jointly model birth weight in grams with a Gaussian likelihood and low birth weight status with a Bernoulli likelihood. Predictive performance was compared with that of conventional single-output regression and classification models.
The heterogeneous multi-output Gaussian process model achieved acceptable predictive performance (R² = 0.67 for birth weight and accuracy = 0.845 for low birth weight classification), with results comparable to those of models fitted separately for each task. These findings support the practical feasibility of modeling heterogeneous outputs within a single probabilistic framework.
In this application, the heterogeneous multi-output Gaussian process model was a viable methodological alternative for jointly modeling birth weight in grams and its binary low-birth-weight classification. This study should be interpreted primarily as a methodological demonstration of a flexible multi-output framework in perinatal data that may be extended in future studies to jointly model other outcomes of greater direct relevance to public health.

PMID:
42371394
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.

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