Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
This study explores machine learning’s potential for early Acute Aortic Syndrome (AAS) prediction by integrating and cleaning extensive clinical datasets from 68 emergency departments in the USA, covering the medical histories of nearly 150,000 patients from 2021 to 2022. Utilizing various data-spli...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/5/257 |
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| author | Mehdi Tavafi Kalpdrum Passi Robert Ohle |
| author_facet | Mehdi Tavafi Kalpdrum Passi Robert Ohle |
| author_sort | Mehdi Tavafi |
| collection | DOAJ |
| description | This study explores machine learning’s potential for early Acute Aortic Syndrome (AAS) prediction by integrating and cleaning extensive clinical datasets from 68 emergency departments in the USA, covering the medical histories of nearly 150,000 patients from 2021 to 2022. Utilizing various data-splitting strategies and classifiers, the research constructs predictive models and addresses dataset size limitations, achieving an exceptional accuracy of 99.3% with the Relief feature method and random forest classifier, facilitating further research on AAS and other cardiovascular diseases. |
| format | Article |
| id | doaj-art-2e5d904c11304f919b2bdafb41d98b8e |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-2e5d904c11304f919b2bdafb41d98b8e2025-08-20T03:47:52ZengMDPI AGAlgorithms1999-48932025-04-0118525710.3390/a18050257Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine LearningMehdi Tavafi0Kalpdrum Passi1Robert Ohle2School of Engineering & Computer Science, Laurentian University, Sudbury, ON P3E 2C6, CanadaSchool of Engineering & Computer Science, Laurentian University, Sudbury, ON P3E 2C6, CanadaDepartment of Emergency Medicine, Health Science North Research Institute, Northern Ontario School of Medicine, Sudbury, ON P3E 5J1, CanadaThis study explores machine learning’s potential for early Acute Aortic Syndrome (AAS) prediction by integrating and cleaning extensive clinical datasets from 68 emergency departments in the USA, covering the medical histories of nearly 150,000 patients from 2021 to 2022. Utilizing various data-splitting strategies and classifiers, the research constructs predictive models and addresses dataset size limitations, achieving an exceptional accuracy of 99.3% with the Relief feature method and random forest classifier, facilitating further research on AAS and other cardiovascular diseases.https://www.mdpi.com/1999-4893/18/5/257machine learningacute aortic syndrome (AAS)clinical dataSMOTE methodfeature extractionPrincipal Component Analysis (PCA) |
| spellingShingle | Mehdi Tavafi Kalpdrum Passi Robert Ohle Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning Algorithms machine learning acute aortic syndrome (AAS) clinical data SMOTE method feature extraction Principal Component Analysis (PCA) |
| title | Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning |
| title_full | Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning |
| title_fullStr | Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning |
| title_full_unstemmed | Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning |
| title_short | Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning |
| title_sort | early risk prediction in acute aortic syndrome on clinical data using machine learning |
| topic | machine learning acute aortic syndrome (AAS) clinical data SMOTE method feature extraction Principal Component Analysis (PCA) |
| url | https://www.mdpi.com/1999-4893/18/5/257 |
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