Using machine learning to examine the relationship between asthma and absenteeism

Maria Anna Lary, Leslie Allsopp, David J. Lary, David A. Sterling

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In this study, we found that machine learning was able to effectively estimate student learning outcomes geo-spatially across all the campuses in a large, urban, independent school district. The machine learning showed that key factors in estimating the student learning outcomes included the number of days students were absent from school. In turn, one of the most important factors in estimating the number of days a student was absent was whether or not the student had asthma. This highlights the importance of environmental public health for student learning outcomes.

Original languageEnglish
Article number332
JournalEnvironmental Monitoring and Assessment
StatePublished - 1 Jun 2019


  • Absenteeism
  • Asthma
  • Environmental & Public Health
  • Learning outcomes
  • Machine learning


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