Predicting Aphasia from Strokes


  • Joseph Jia Scholar Launch
  • Joanna Gilberti Mentor, New York University



Machine Learning, High School, Aphasia, Stroke, After-Stroke Effects


Strokes can occur when someone’s blood vessels get blocked and the nutrients and oxygen being transported will not reach the brain. When a stroke happens, the brain cells don’t get the nutrients they need and start to die [3]. This could cause different side effects after stroke. In this study, we try to predict the possibility of one type of after-stroke side effect, aphasia, using Machine Learning (ML) techniques. Using the data of a study about brain lesion damage after a stroke and what effects the patients were experiencing afterward, we trained a model to predict whether a person may have aphasia based on where their lesion was, how big the lesion was, how long ago their stroke was, and some other factors. We evaluated several classification methods and found that using linear discriminant analysis was the most accurately predicting when we used age, sex, lesion location, lesion volume, and many more. By linear discriminant analysis, we were able to have a 91% overall predictive rate of patients having aphasia or not after experiencing a stroke.


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References or Bibliography

Frenkel-Toledo S, Fridberg G, Ofir S, Bartur G, Lowenthal-Raz J, et al. (2019) Lesion location impact on functional recovery of the hemiparetic upper limb. PLOS ONE 14(7): e0219738.

Benjamin, Emelia J., and Michael J. Blaha, et al. “Circulation.” CDC Stacks, 25 Jan.

, Accessed 26 Dec. 2020.

”About Stroke.”, American Heart Association,

”Aphasia.” National Institution on Deafness and Other Communications Disorders,

U.S. Department of Health and Human Services, Dec. 2015,

health/aphasia. Accessed 26 Dec. 2020.

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How to Cite

Jia, J., & Gilberti, J. (2021). Predicting Aphasia from Strokes. Journal of Student Research, 10(3).



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