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Predicting stroke risk by Migraine using AI

Author

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  • Anchal Singh
  • Surabhi Thorat

Abstract

Stroke is a blood clot or bleeds in the brain, which can make permanent damage that has an effect on mobility, cognition, sight or communication. It is the second leading cause of death worldwide and one of the most life- threatening diseases for persons above 65 years. It damages the brain like “heart attack” which damages the heart. Every 4 minutes someone dies of stroke, but up to 80% of stroke can be prevented if we can identify or predict the occurrence of stroke in its early stage. In this paper, I used different types of machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke data. Four types of machine learning classification algorithms were applied; Linear Regression, Confusion matrices, Random Forest Classifier, and Logistic Regression were used to build the stroke prediction model. Support, Precision, Recall, and F1-score were used to calculate performance measures of machine learning models. The results showed that Random Forest Classifier has achieved the best accuracy at 94 % [1].

Suggested Citation

  • Anchal Singh & Surabhi Thorat, 2021. "Predicting stroke risk by Migraine using AI," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 7(6), pages 285-290, December.
    • Handle: RePEc:jbh:ijsrcs:v7:y2021:i6:id:hcseit217656
    DOI: 10.32628/CSEIT217656
    Note: Article URL: https://ijsrcseit.com/CSEIT217656
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