On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes
This study aims to review retrospectively the records of Asian patients diagnosed with abdominal aortic aneurysm to investigate the potential correlations between clinical and morphological parameters within the context of whether the aneurysms were ruptured or unruptured. A machine-learning-based a...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2020
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| Online Access: | https://hdl.handle.net/10356/137200 |
| _version_ | 1811676750327840768 |
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| author | Canchi, Tejas Ng, Eddie Yin Kwee Narayanan, Sriram Finol, Ender A. |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Canchi, Tejas Ng, Eddie Yin Kwee Narayanan, Sriram Finol, Ender A. |
| author_sort | Canchi, Tejas |
| collection | NTU |
| description | This study aims to review retrospectively the records of Asian patients diagnosed with abdominal aortic aneurysm to investigate the potential correlations between clinical and morphological parameters within the context of whether the aneurysms were ruptured or unruptured. A machine-learning-based approach is proposed to predict the rupture status of Asian abdominal aortic aneurysm by comparing four different classifiers trained with clinical and geometrical parameters obtained from computed tomography images. The classifiers were applied on 312 patient data sets obtained from a regulatory-approved database. The data sets included 17 attributes under three classes: unruptured abdominal aortic aneurysm, ruptured abdominal aortic aneurysm, and normal aorta without aneurysm. Four different classification models, namely, Decision trees, Naïve Bayes, logistic regression, and support vector machines were applied to the patient data set. The models were evaluated by 10-fold cross-validation and the classifier performances were assessed with classification accuracy, area under the curve of receiver operator characteristic, and F-measures. Data analysis and evaluation were performed using the Weka machine learning application. The results indicated that Naïve Bayes achieved the best performance among the classifiers with a classification accuracy of 95.2%, an area under the curve of 0.974, and an F-measure of 0.952. The clinical implications of this work can be addressed in two ways. The best classifier can be applied to prospectively acquired data to predict the likelihood of aneurysm rupture. Next, it would be necessary to estimate the attributes implicated in rupture risk beyond just maximum aneurysm diameter. |
| first_indexed | 2024-10-01T02:26:26Z |
| format | Journal Article |
| id | ntu-10356/137200 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:26:26Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1372002023-03-04T17:22:25Z On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes Canchi, Tejas Ng, Eddie Yin Kwee Narayanan, Sriram Finol, Ender A. School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Abdominal Aortic Aneurysm Rupture Risk This study aims to review retrospectively the records of Asian patients diagnosed with abdominal aortic aneurysm to investigate the potential correlations between clinical and morphological parameters within the context of whether the aneurysms were ruptured or unruptured. A machine-learning-based approach is proposed to predict the rupture status of Asian abdominal aortic aneurysm by comparing four different classifiers trained with clinical and geometrical parameters obtained from computed tomography images. The classifiers were applied on 312 patient data sets obtained from a regulatory-approved database. The data sets included 17 attributes under three classes: unruptured abdominal aortic aneurysm, ruptured abdominal aortic aneurysm, and normal aorta without aneurysm. Four different classification models, namely, Decision trees, Naïve Bayes, logistic regression, and support vector machines were applied to the patient data set. The models were evaluated by 10-fold cross-validation and the classifier performances were assessed with classification accuracy, area under the curve of receiver operator characteristic, and F-measures. Data analysis and evaluation were performed using the Weka machine learning application. The results indicated that Naïve Bayes achieved the best performance among the classifiers with a classification accuracy of 95.2%, an area under the curve of 0.974, and an F-measure of 0.952. The clinical implications of this work can be addressed in two ways. The best classifier can be applied to prospectively acquired data to predict the likelihood of aneurysm rupture. Next, it would be necessary to estimate the attributes implicated in rupture risk beyond just maximum aneurysm diameter. Accepted version 2020-03-06T03:15:09Z 2020-03-06T03:15:09Z 2018 Journal Article Canchi, T., Ng, E. Y. K., Narayanan, S., & Finol, E. A. (2018). On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 232(9), 922-929. doi:10.1177/0954411918794724 0954-4119 https://hdl.handle.net/10356/137200 10.1177/0954411918794724 30122103 2-s2.0-85052574246 9 232 922 929 en Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine © 2018 IMechE. All rights reserved. This paper was published in Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine and is made available with permission of IMechE. application/pdf |
| spellingShingle | Engineering::Mechanical engineering Abdominal Aortic Aneurysm Rupture Risk Canchi, Tejas Ng, Eddie Yin Kwee Narayanan, Sriram Finol, Ender A. On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes |
| title | On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes |
| title_full | On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes |
| title_fullStr | On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes |
| title_full_unstemmed | On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes |
| title_short | On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes |
| title_sort | on the assessment of abdominal aortic aneurysm rupture risk in the asian population based on geometric attributes |
| topic | Engineering::Mechanical engineering Abdominal Aortic Aneurysm Rupture Risk |
| url | https://hdl.handle.net/10356/137200 |
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