Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment
As a leading cause of death, strokes have been regarded as a dangerously impactful condition with little to no predictability. Currently, there is no effective method to predict a stroke using warning signs and hereditary factors. We developed a quantitative method to predict strokes before happenin...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/89/1/2 |
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| Summary: | As a leading cause of death, strokes have been regarded as a dangerously impactful condition with little to no predictability. Currently, there is no effective method to predict a stroke using warning signs and hereditary factors. We developed a quantitative method to predict strokes before happening. We used MRI scan data obtained from OpenNeuro, specifically images showing the signs of pre-stroke and post-stroke. We trained machine learning models using the data, including support vector machines (SVMs), K-nearest neighbors (KNNs), and random forests. The models predicted the risk of a stroke accurately. The models allow for diagnosing and enable clinicians to care for patients promptly, potentially saving lives and improving outcomes. |
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| ISSN: | 2673-4591 |