Maternal and child health journal · 2026
Multiple Output Gaussian Process Model for Predicting Low Birth Weight in Medellín, Colombia: An Alternative to Conventional Machine Learning Models.
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Salazar Blandon Diego Alejandro, García Arias Hernán Felipe, Giraldo Gutiérrez Juan José
Аннотация
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.