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

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

AimsThe objective of this study is to illustrate the application of a machine learning algorithm, K Nearest Neighbor (k-NN) to impute missing alcohol data in a prospective study among pregnant women.MethodsWe used data from the Safe Passage study (n = 11,083). Daily alcohol consumption for the last...

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Bibliographic Details
Main Authors: Ayesha Sania, Nicolò Pini, Morgan E. Nelson, Michael M. Myers, Lauren C. Shuffrey, Maristella Lucchini, Amy J. Elliott, Hein J. Odendaal, William P. Fifer
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Advances in Drug and Alcohol Research
Subjects:
k nearest neighbor
k-NN
machine learning
data missingness
data imputation
prenatal alcohol data
Online Access:https://www.frontierspartnerships.org/articles/10.3389/adar.2024.13449/full
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https://www.frontierspartnerships.org/articles/10.3389/adar.2024.13449/full

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