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

Arantxa Ortega-Leon (), Arnaud Gucciardi, Antonio Segado-Arenas, Isabel Benavente-Fernández, Daniel Urda and Ignacio J. Turias ()
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Arantxa Ortega-Leon: Intelligent Modelling of Systems Research Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, 11202 Algeciras, Spain
Arnaud Gucciardi: Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
Antonio Segado-Arenas: Department of Paediatrics, Neonatology Section, Puerta del Mar University Hospital, 11008 Cádiz, Spain
Isabel Benavente-Fernández: Department of Paediatrics, Neonatology Section, Puerta del Mar University Hospital, 11008 Cádiz, Spain
Daniel Urda: Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain
Ignacio J. Turias: Intelligent Modelling of Systems Research Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, 11202 Algeciras, Spain

Stats, 2024, vol. 7, issue 3, 1-12

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.

Keywords: preterm infants; neurodevelopmental impairment; machine learning (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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