AUTHOR=Bowe Andrea K. , Lightbody Gordon , Staines Anthony , Kiely Mairead E. , McCarthy Fergus P. , Murray Deirdre M. 

TITLE=Predicting Low Cognitive Ability at Age 5—Feature Selection Using Machine Learning Methods and Birth Cohort Data

JOURNAL=International Journal of Public Health

VOLUME=Volume 67 - 2022

YEAR=2022

URL=https://www.ssph-journal.org/journals/international-journal-of-public-health/articles/10.3389/ijph.2022.1605047

DOI=10.3389/ijph.2022.1605047

ISSN=1661-8564

ABSTRACT=Objectives
In this study, we applied the random forest (RF) algorithm to birth-cohort data to train a model to predict low cognitive ability at 5 years of age and to identify the important predictive features.
Methods
Data was from 1,070 participants in the Irish population-based BASELINE cohort. A RF model was trained to predict an intelligence quotient (IQ) score 90 at age 5 years using maternal, infant, and sociodemographic features. Feature importance was examined and internal validation performed using 10-fold cross validation repeated 5 times.
Results
The five most important predictive features were the total years of maternal schooling, infant Apgar score at 1 minute, socioeconomic index, maternal BMI, and alcohol consumption in the first trimester. On internal validation a parsimonious RF model based on 11 features showed excellent predictive ability, correctly classifying 95% of participants. This provides a foundation suitable for external validation in an unseen cohort. 
Conclusions
Machine learning approaches to large existing datasets can provide accurate feature selection to improve risk prediction. Further validation of this model is required in cohorts representative of the general population.