Machine learning predicts kids at risk of not getting vaccinated


EXCERPT: Growing skepticism toward vaccines has sparked a flareup of measles outbreaks affecting New York City neighborhoods, cruise ships, international airports and even Google’s Mountain View headquarters. To help family physicians reach out to vaccine-hesitant parents, data scientists have shown how computer models can predict the likelihood that an individual child’s parents will not get him or her vaccinated. [...] By identifying families at greatest risk of not getting vaccinated, computer models could enable health officials and physicians to talk with parents at the stage when they remain undecided about vaccines.

To help boost vaccination rates, Tin Oreskovic initiated and coordinated a University of Chicago Data Science for Social Good project [...] After comparing the results from four machine learning models, researchers decided upon a LASSO logistic regression model that identified vaccine-hesitant families with 72-percent precision. The model pruned the large number of possible data features affecting vaccination rates down to just 25 of the most important features—something that improved the chance of the model’s predictive power holding up for other groups of children beyond those in the training datasets. (Some features that raised child risk scores included having children who sat, walked, and spoke at a later age than their peers.)

Just as importantly, the team chose the LASSO model because it presented the results for child risk scores in a way that humans could understand. Interpretability is never a guarantee with many machine learning models, but in this case it allowed both data scientists and health officials to understand and trust the LASSO model’s reasons for singling out certain families as being at higher risk of hesitating to vaccinate... (MORE)

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