By
Anuradha T.,
Abhishek J.,
, Maheboob Patel,
Mohammad Akbar Ali,
- Student, Poojya Doddappa Appa College of Engineering Kalaburagi, Karnataka, India
- Student, Poojya Doddappa Appa College of Engineering Kalaburagi, Karnataka, India
- Student, Poojya Doddappa Appa College of Engineering Kalaburagi, Karnataka, India
- Student, Poojya Doddappa Appa College of Engineering Kalaburagi, Karnataka, India
Abstract
Globally, strokes are the primary cause of disability and mortality. Recently, machine learning (ML) and deep learning (DL) have been employed by artificial intelligence algorithms as effective stroke diagnosing techniques. These days, machine learning and data mining technologies are used in the construction of the main models. We have used five machine learning algorithms to determine if a stroke has occurred or is likely to occur based on a patient’s physical state and information from medical reports. We are utilizing the numerous hospitals records we have gathered to address our issue. The categorization outcome demonstrates that the outcome is appropriate and useful for a real-time medical report. We discovered that machine learning algorithms can help us comprehend diseases and be a helpful tool in the healthcare industry.
Keywords: Random forest, k-NN, artificial intelligence, decision tree, logistic regression, SVM
[This article belongs to Journal of Microwave Engineering and Technologies(jomet)]
How to cite this article: Anuradha T., Abhishek J., , Maheboob Patel, Mohammad Akbar Ali. Identification of Brain Stroke Using Artificial Intelligence. Journal of Microwave Engineering and Technologies. 2024; 11(02):15-22.
How to cite this URL: Anuradha T., Abhishek J., , Maheboob Patel, Mohammad Akbar Ali. Identification of Brain Stroke Using Artificial Intelligence. Journal of Microwave Engineering and Technologies. 2024; 11(02):15-22. Available from: https://journals.stmjournals.com/jomet/article=2024/view=167132
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Regular Issue Subscription Review Article
Journal of Microwave Engineering and Technologies
ISSN: 2349-9001
| Volume | 11 |
| Issue | 02 |
| Received | July 6, 2024 |
| Accepted | July 19, 2024 |
| Published | August 14, 2024 |