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

ABSTRACT:

Aims

The 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.

Methods

We used data from the Safe Passage study (n = 11,083). Daily alcohol consumption for the last reported drinking day and 30 days prior was recorded using the Timeline Follow back method, which generated a variable amount of missing data per participants. Of the 3.2 million person-days of observation, data were missing for 0.36 million (11.4%). Using the k-NN imputed values were weighted for the distances and matched for the day of the week. Since participants with no missing days were not comparable to those with missing data, segments of non-missing data f

SUBMITTER: Sania A 

PROVIDER: S-EPMC11811783 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature