Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients
Abstract Patients with acute ischemic stroke can benefit from reperfusion therapy. Nevertheless, there are gray areas where initiation of reperfusion therapy is neither supported nor contraindicated by the current practice guidelines. In these situations, a prediction model for mortality can be bene...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-22323-9 |
| _version_ | 1811336504626118656 |
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| author | Lee Hwangbo Yoon Jung Kang Hoon Kwon Jae Il Lee Han-Jin Cho Jun-Kyeung Ko Sang Min Sung Tae Hong Lee |
| author_facet | Lee Hwangbo Yoon Jung Kang Hoon Kwon Jae Il Lee Han-Jin Cho Jun-Kyeung Ko Sang Min Sung Tae Hong Lee |
| author_sort | Lee Hwangbo |
| collection | DOAJ |
| description | Abstract Patients with acute ischemic stroke can benefit from reperfusion therapy. Nevertheless, there are gray areas where initiation of reperfusion therapy is neither supported nor contraindicated by the current practice guidelines. In these situations, a prediction model for mortality can be beneficial in decision-making. This study aimed to develop a mortality prediction model for acute ischemic stroke patients not receiving reperfusion therapies using a stacking ensemble learning model. The model used an artificial neural network as an ensemble classifier. Seven base classifiers were K-nearest neighbors, support vector machine, extreme gradient boosting, random forest, naive Bayes, artificial neural network, and logistic regression algorithms. From the clinical data in the International Stroke Trial database, we selected a concise set of variables assessable at the presentation. The primary study outcome was all-cause mortality at 6 months. Our stacking ensemble model predicted 6-month mortality with acceptable performance in ischemic stroke patients not receiving reperfusion therapy. The area under the curve of receiver-operating characteristics, accuracy, sensitivity, and specificity of the stacking ensemble classifier on a put-aside validation set were 0.783 (95% confidence interval 0.758–0.808), 71.6% (69.3–74.2), 72.3% (69.2–76.4%), and 70.9% (68.9–74.3%), respectively. |
| first_indexed | 2024-04-13T17:40:32Z |
| format | Article |
| id | doaj.art-54bc36818bd948469ab35965b5db50a6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T17:40:32Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-54bc36818bd948469ab35965b5db50a62022-12-22T02:37:11ZengNature PortfolioScientific Reports2045-23222022-10-011211910.1038/s41598-022-22323-9Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patientsLee Hwangbo0Yoon Jung Kang1Hoon Kwon2Jae Il Lee3Han-Jin Cho4Jun-Kyeung Ko5Sang Min Sung6Tae Hong Lee7Department of Radiology, Pusan National University HospitalDepartment of Neurology, Pusan National University HospitalDepartment of Radiology, Pusan National University HospitalDepartment of Neurosurgery, Pusan National University HospitalDepartment of Neurology, Pusan National University HospitalDepartment of Neurosurgery, Pusan National University HospitalDepartment of Neurology, Pusan National University HospitalDepartment of Radiology, Pusan National University HospitalAbstract Patients with acute ischemic stroke can benefit from reperfusion therapy. Nevertheless, there are gray areas where initiation of reperfusion therapy is neither supported nor contraindicated by the current practice guidelines. In these situations, a prediction model for mortality can be beneficial in decision-making. This study aimed to develop a mortality prediction model for acute ischemic stroke patients not receiving reperfusion therapies using a stacking ensemble learning model. The model used an artificial neural network as an ensemble classifier. Seven base classifiers were K-nearest neighbors, support vector machine, extreme gradient boosting, random forest, naive Bayes, artificial neural network, and logistic regression algorithms. From the clinical data in the International Stroke Trial database, we selected a concise set of variables assessable at the presentation. The primary study outcome was all-cause mortality at 6 months. Our stacking ensemble model predicted 6-month mortality with acceptable performance in ischemic stroke patients not receiving reperfusion therapy. The area under the curve of receiver-operating characteristics, accuracy, sensitivity, and specificity of the stacking ensemble classifier on a put-aside validation set were 0.783 (95% confidence interval 0.758–0.808), 71.6% (69.3–74.2), 72.3% (69.2–76.4%), and 70.9% (68.9–74.3%), respectively.https://doi.org/10.1038/s41598-022-22323-9 |
| spellingShingle | Lee Hwangbo Yoon Jung Kang Hoon Kwon Jae Il Lee Han-Jin Cho Jun-Kyeung Ko Sang Min Sung Tae Hong Lee Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients Scientific Reports |
| title | Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients |
| title_full | Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients |
| title_fullStr | Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients |
| title_full_unstemmed | Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients |
| title_short | Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients |
| title_sort | stacking ensemble learning model to predict 6 month mortality in ischemic stroke patients |
| url | https://doi.org/10.1038/s41598-022-22323-9 |
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