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

Abstract

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.