A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants

Aug 4, 2026

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Psychomotor developmental delay in infants involves missing key abilities such as sitting, walking, grasping, and communication at ages when most infants achieve these milestones. Risk factors include family environment, socioeconomic position, pregnancy and birth problems, and maternal health, making early screening clinically valuable so that timely healthcare interventions can be considered. The study applies machine learning (random forest) to predict delay in 9-month-old infants using data available at birth and early infancy from the UK Millennium Cohort study.

LanguageEnglish

CategoryResearch & Report

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Pages13

Reading Time33 min

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Introduction

Predictors of Developmental Delay

Bond University Research Repository A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants Waynforth, David Published in: Reproductive Medicine DOI: 10.3390/reprodmed4020012 Licence: CC BY Link to output in Bond University research repository. Recommended citation(APA): Waynforth, D. (2023) . A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants. Reproductive Medicine, 4(2), 106-117. [https://doi.org/10.3390/reprodmed4020012](https://doi.org/10.3390/reprodmed4020012) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. For more information, or if you believe that this document breaches copyright, please contact the Bond University research repository coordinator. Download date: 04 Aug 2026 reproductive medicine Article A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants David Waynforth Citation: Waynforth, D. A Machine Learning Algorithm Predicting Infant Psychomotor Developmental Delay Using Medical and Social Determinants. Reprod. Med. 2023, 4, 106–117 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)reprodmed4020012 Academic Editor: Berthold Huppertz Received: 29 March 2023 Revised: 30 May 2023 Accepted: 2 June 2023 Published: 5 June 2023 Copyright: © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . School of Medicine, Faculty of Health Sciences, Bond University, Gold Coast, QLD 4226, Australia; [dwaynfor@bond.edu.au](dwaynfor@bond.edu.au) Abstract: Psychomotor developmental delay in infants includes failure to acquire abilities such as sitting, walking, grasping objects and communication at the ages when most infants have acquired these abilities. Known risk factors include a large number of aspects of family environment, socioeconomic position, problems in pregnancy and birth and maternal health. It is clinically useful to be able to screen for developmental delay so that healthcare interventions can be considered. The present research used machine learning (random forest) to create an algorithm predicting psychomotor delay in 9-month-old infants using information ascertainable at birth and in early infancy. The dataset was the UK longitudinal Millennium Cohort study. In total, 53 predictors measuring socioeconomic indicators, paternal, family and social support for the mother, beliefs about good parenting, maternal health, pregnancy and birth were included in the initial algorithm. Feature reduction showed that of the 53 variables, birthweight, gestational age at birth, pre-pregnancy BMI, family income and parents'ages had the highest feature importance scores and could alone correctly predict developmental delay with over 99% sensitivity and 100% speciﬁcity. No features measuring aspects of early infant care or environment meaningfully added to algorithm performance. The relationships between delay and some of the predictors, particularly income, were nonlinear and complex. The results suggest that the risk of psychomotor developmental delay can be identiﬁed in early infancy using machine learning, and that the best predictors are factors present prior to and at birth. Keywords: developmental milestones; artiﬁcial intelligence; classiﬁcation algorithms; infant growth 1. Introduction Children's progress in achiev

FAQs

What does the study aim to predict for infants?

It predicts psychomotor developmental delay in 9-month-old infants, focusing on whether key milestones will be achieved at the expected ages.

Which machine learning method and dataset are used?

A random forest algorithm is trained using the UK longitudinal Millennium Cohort study data.

Which factors were most important for predicting developmental delay?

Birthweight, gestational age at birth, pre-pregnancy BMI, family income, and parents' ages showed the highest feature importance and could predict delay with very high sensitivity and specificity.

Aug 4, 2026

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