Neurodevelopmental Impairments Prediction in Premature Infants Based on Clinical Data and Machine Learning Techniques

Abstract

Preterm infants are prone to NeuroDevelopmental Impairment (NDI). Some previous works have identified clinical variables that can be potential predictors of NDI. However, machine learning (ML)-based models still present low predictive capabilities when addressing this problem. This work attempts to evaluate the application of ML techniques to predict NDI using clinical data from a cohort of very preterm infants recruited at birth and assessed at 2 years of age. Six different classification models were assessed, using all features, clinician-selected features, and mutual information feature selection. The best results were obtained by ML models trained using mutual information-selected features and employing oversampling, for cognitive and motor impairment prediction, while for language impairment prediction the best setting was clinician-selected features. Although the performance indicators in this local cohort are consistent with similar previous works and still rather poor. This is a clear indication that, in order to obtain better performance rates, further analysis and methods should be considered, and other types of data should be taken into account together with the clinical variables.