International Journal of Public Health (Nov 2022)

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

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

DOI
https://doi.org/10.3389/ijph.2022.1605047
Journal volume & issue
Vol. 67

Abstract

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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 min, 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.Conclusion: 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.

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