Early prediction of ADHD symptoms from perinatal characteristics: A machine learning study
Yee-Lam Ho1, Bonnie Auyeung1, Aja Murray1
1Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburghhttps://ror.org/01nrxwf90, Edinburgh, UK.
Insights
Early identification of attention-deficit hyperactivity disorder (ADHD) symptoms is possible using birth data. Infant male sex and maternal smoking during pregnancy were key predictors in this study.
Area of Science:
- Pediatrics
- Neurodevelopmental Disorders
- Public Health
Background:
- Early identification of attention-deficit hyperactivity disorder (ADHD) symptoms is crucial for timely interventions and improved long-term outcomes.
- Previous research has identified various maternal and perinatal factors associated with ADHD, but comprehensive analyses examining these predictors collectively are limited.
- Understanding early risk factors can inform preventative strategies and support for affected children and families.
Purpose of the Study:
- To assess the predictability of later ADHD symptoms from information available at birth, including ethnicity, maternal metabolic markers, mental health, and socioeconomic status.
- To identify the most influential predictors of ADHD symptoms using a combined analysis of maternal and perinatal data.
- To establish a proof of principle for early ADHD risk identification using routinely collected data.
Main Methods:
- Utilized data from the Born in Bradford (BiB) cohort study.
- Applied multiple linear regression (LR) and machine learning techniques (random forest) to predict ADHD symptoms.
- Measured ADHD symptoms using the Hyperactivity/Inattention subscale of the Strengths and Difficulties Questionnaire (SDQ).
Main Results:
- A 10-fold cross-validated LR model explained 6.97% of the variance in SDQ scores.
- The random forest model identified infant male sex and maternal smoking during pregnancy as the most significant predictors of ADHD symptoms.
- These findings demonstrate the potential of using birth-related information for early risk assessment.
Conclusions:
- The study provides proof of principle for the early identification of children at risk for ADHD symptoms.
- Infant sex and maternal smoking during pregnancy are key early predictors of ADHD symptoms.
- Incorporating additional perinatal data into predictive models may enhance accuracy for early ADHD risk assessment.
Abstract:
Early identification of risk for attention-deficit hyperactivity disorder (ADHD) symptoms can enable more timely interventions and improve long-term outcomes. While previous research has linked various maternal and perinatal factors to ADHD, few studies have examined these predictors collectively in a single comprehensive analysis. This study aimed to assess whether later ADHD symptoms can be predicted from information available at birth, specifically ethnicity, maternal metabolic markers, mental health, and socioeconomic status. It additionally aimed to identify the most influential predictors. Using data from the Born in Bradford (BiB) study, we applied multiple linear regression (LR) and machine learning techniques to predict ADHD symptoms as measured by the Hyperactivity/Inattention subscale of the Strengths and Difficulties Questionnaire (SDQ). A 10-fold cross-validated LR model explained 6.97% of the variance in SDQ scores. In the random forest model, infant male sex and maternal smoking during pregnancy emerged as the top predictors. These findings provide proof of principle for early identification of children at risk of ADHD. Future models may benefit from incorporating additional perinatal data to improve predictive accuracy.
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