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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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/89/1/2 |
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| author | Pranav Kunderu Salik Mian Shivm Patel |
| author_facet | Pranav Kunderu Salik Mian Shivm Patel |
| author_sort | Pranav Kunderu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-49db46048acd4a4f82c61bc5f2bd3071 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-49db46048acd4a4f82c61bc5f2bd30712025-08-20T03:27:28ZengMDPI AGEngineering Proceedings2673-45912025-02-01891210.3390/engproc2025089002Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced AssessmentPranav Kunderu0Salik Mian1Shivm Patel2Independent Researcher, Los Angeles, CA 91307, USAIndependent Researcher, Los Angeles, CA 91326, USAMolecular & Cell Biology, University of California, Berkeley, CA 94720, USAAs 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.https://www.mdpi.com/2673-4591/89/1/2MRIstrokesupport vector machines (SVMs)k-nearest neighbors (KNNs)random forests |
| spellingShingle | Pranav Kunderu Salik Mian Shivm Patel Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment Engineering Proceedings MRI stroke support vector machines (SVMs) k-nearest neighbors (KNNs) random forests |
| title | Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment |
| title_full | Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment |
| title_fullStr | Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment |
| title_full_unstemmed | Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment |
| title_short | Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment |
| title_sort | enhancing stroke risk prediction leveraging machine learning and magnetic resonance imaging data for advanced assessment |
| topic | MRI stroke support vector machines (SVMs) k-nearest neighbors (KNNs) random forests |
| url | https://www.mdpi.com/2673-4591/89/1/2 |
| work_keys_str_mv | AT pranavkunderu enhancingstrokeriskpredictionleveragingmachinelearningandmagneticresonanceimagingdataforadvancedassessment AT salikmian enhancingstrokeriskpredictionleveragingmachinelearningandmagneticresonanceimagingdataforadvancedassessment AT shivmpatel enhancingstrokeriskpredictionleveragingmachinelearningandmagneticresonanceimagingdataforadvancedassessment |