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K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data.

Ayesha Sania1,2, Nicolò Pini1,2, Morgan E Nelson3

  • 1Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.

Advances in Drug and Alcohol Research
|February 12, 2025
PubMed
Summary

The K Nearest Neighbor (k-NN) algorithm accurately imputes missing alcohol consumption data in pregnant women. This machine learning approach improves data completeness for longitudinal pregnancy studies.

Keywords:
data imputationdata missingnessk nearest neighbork-NNmachine learningprenatal alcohol data

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Machine Learning

Background:

  • Longitudinal studies on pregnant women often face challenges with missing alcohol consumption data.
  • Inaccurate or incomplete data can bias findings in studies of prenatal alcohol exposure.
  • Robust imputation methods are crucial for reliable analysis of such data.

Purpose of the Study:

  • To evaluate the effectiveness of the K Nearest Neighbor (k-NN) machine learning algorithm for imputing missing daily alcohol consumption data.
  • To assess the accuracy of k-NN imputation in a large prospective cohort of pregnant women (Safe Passage study).
  • To determine optimal parameters for k-NN imputation to minimize error.

Main Methods:

  • Utilized data from the Safe Passage study (n=11,083) with missing alcohol consumption data (11.4%).
  • Applied the k-NN algorithm, weighting distances and matching by day of week for imputation.
  • Validated imputation accuracy by randomly deleting data segments and comparing imputed to actual values.

Main Results:

  • The k-NN algorithm with 5 nearest neighbors (K=5) and 55-day segments yielded the lowest imputation error.
  • Imputed values closely matched actual values for 64% of deleted segments and were within +/-1 drink/day for 31%.
  • Imputation accuracy demonstrated variability across study sites due to differences in drinking patterns and missing data proportions.

Conclusions:

  • The K Nearest Neighbor (k-NN) algorithm offers a highly accurate method for imputing missing alcohol data in longitudinal pregnancy studies.
  • k-NN imputation can significantly enhance the quality and reliability of data in studies examining alcohol use during pregnancy.
  • This machine learning approach provides a valuable tool for addressing data gaps in sensitive public health research.