Employability of Ensembled-Based Artificial Intelligence System in the prediction of Brain Stroke
Dhruv Khera
Pathways School, Noida, India
26-33
Vol: 11, Issue: 4, 2021
Receiving Date:
2021-07-24
Acceptance Date:
2021-09-29
Publication Date:
2021-10-26
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http://doi.org/10.37648/ijrst.v11i04.004
Abstract
Early detection and proactive stroke prevention measures are essential due to the significant risk of severe disabilities or fatal outcomes. Strokes that are ischemic or hemorrhagic necessitate the prompt administration of the appropriate thrombolytic or anticoagulant medication. The crucial and initial stage involves seeking medical attention within the prescribed treatment window and promptly recognizing the individual-specific initial signs of a stroke. This study presents a machine learning-based system for predicting and meaningfully interpreting prognostic stroke symptoms based on real-time electrocardiogram (ECG) and photoplethysmography (PPG) data measurements. We have made and carried out a gathering structure casting a ballot classifier that joins SVM, Irregular Woodland, and choice tree classifiers to accomplish continuous stroke expectation. This method accurately predicts stroke diagnosis and is simple to implement thanks to the patient's ECG and PPG attribute data.
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