Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes
<p dir="ltr">Accurately predicting the severity of subarachnoid hemorrhage (SAH) is critical for informing clinical decisions and improving patient outcomes. This study addresses the challenges of imbalanced data in SAH severity classification by employing the Modified Rankin Scale (...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , , , , , , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513533125853184 |
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| author | Muhammad Mohsin Khan (22150360) |
| author2 | Adiba Tabassum Chowdhury (19444792) Md. Shaheenur Islam Sumon (17983810) Shaikh Nissaruddin Maheboob (22565696) Arshad Ali (638656) Abdul Nasser Thabet (22565699) Ghaya Al-Rumaihi (22565702) Sirajeddin Belkhair (17151106) Ghanem AlSulaiti (22565705) Ali Ayyad (149042) Noman Shah (22150363) Anwarul Hasan (1332066) Shona Pedersen (2792278) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author author author author author author author |
| author_facet | Muhammad Mohsin Khan (22150360) Adiba Tabassum Chowdhury (19444792) Md. Shaheenur Islam Sumon (17983810) Shaikh Nissaruddin Maheboob (22565696) Arshad Ali (638656) Abdul Nasser Thabet (22565699) Ghaya Al-Rumaihi (22565702) Sirajeddin Belkhair (17151106) Ghanem AlSulaiti (22565705) Ali Ayyad (149042) Noman Shah (22150363) Anwarul Hasan (1332066) Shona Pedersen (2792278) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Mohsin Khan (22150360) Adiba Tabassum Chowdhury (19444792) Md. Shaheenur Islam Sumon (17983810) Shaikh Nissaruddin Maheboob (22565696) Arshad Ali (638656) Abdul Nasser Thabet (22565699) Ghaya Al-Rumaihi (22565702) Sirajeddin Belkhair (17151106) Ghanem AlSulaiti (22565705) Ali Ayyad (149042) Noman Shah (22150363) Anwarul Hasan (1332066) Shona Pedersen (2792278) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2025-07-10T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10143-025-03678-9 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multi-class_subarachnoid_hemorrhage_severity_prediction_addressing_challenges_in_predicting_rare_outcomes/30542501 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Subarachnoid hemorrhage Severity prediction Modified rankin scale Imbalanced data Electronic health record |
| dc.title.none.fl_str_mv | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Accurately predicting the severity of subarachnoid hemorrhage (SAH) is critical for informing clinical decisions and improving patient outcomes. This study addresses the challenges of imbalanced data in SAH severity classification by employing the Modified Rankin Scale (MRS) within a three-stage classification framework. We utilize a three-stage approach to effectively categorize SAH severity. In the first stage, we performed binary classification, grouping SAH severity into “Good Outcome” (class 0), which includes MRS levels 0, 1, 2, and 3, and “Poor Outcome” (class 1), encompassing levels 4, 5, and 6. Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. We evaluated thirteen machine learning models at each stage, selecting the top-performing classifiers to optimize results. The dataset comprised 535 samples across seven MRS severity levels and was validated using 5-fold cross-validation and diverse subgroups to ensure robust model performance across various scenarios. Binary classification in the first stage achieved approximately 90% accuracy with Extra Trees. In the second stage, targeting the “Good Outcome” group, the Random Forest model reached 88% accuracy, while in the third stage, it achieved 86% accuracy for the “Poor Outcome” group. By increasing accuracy across unbalanced classes and emphasizing its potential for practical use, the multi-stage technique presents a promising solution for predicting the severity of SAH. Future research will concentrate on additional tuning to improve the model’s efficacy in actual healthcare environments.</p><h2>Other Information</h2><p dir="ltr">Published in: Neurosurgical Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10143-025-03678-9" target="_blank">https://dx.doi.org/10.1007/s10143-025-03678-9</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_996b5d91dcd7377d788075a3a9fb4143 |
| identifier_str_mv | 10.1007/s10143-025-03678-9 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30542501 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomesMuhammad Mohsin Khan (22150360)Adiba Tabassum Chowdhury (19444792)Md. Shaheenur Islam Sumon (17983810)Shaikh Nissaruddin Maheboob (22565696)Arshad Ali (638656)Abdul Nasser Thabet (22565699)Ghaya Al-Rumaihi (22565702)Sirajeddin Belkhair (17151106)Ghanem AlSulaiti (22565705)Ali Ayyad (149042)Noman Shah (22150363)Anwarul Hasan (1332066)Shona Pedersen (2792278)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesNeurosciencesHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningSubarachnoid hemorrhageSeverity predictionModified rankin scaleImbalanced dataElectronic health record<p dir="ltr">Accurately predicting the severity of subarachnoid hemorrhage (SAH) is critical for informing clinical decisions and improving patient outcomes. This study addresses the challenges of imbalanced data in SAH severity classification by employing the Modified Rankin Scale (MRS) within a three-stage classification framework. We utilize a three-stage approach to effectively categorize SAH severity. In the first stage, we performed binary classification, grouping SAH severity into “Good Outcome” (class 0), which includes MRS levels 0, 1, 2, and 3, and “Poor Outcome” (class 1), encompassing levels 4, 5, and 6. Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. We evaluated thirteen machine learning models at each stage, selecting the top-performing classifiers to optimize results. The dataset comprised 535 samples across seven MRS severity levels and was validated using 5-fold cross-validation and diverse subgroups to ensure robust model performance across various scenarios. Binary classification in the first stage achieved approximately 90% accuracy with Extra Trees. In the second stage, targeting the “Good Outcome” group, the Random Forest model reached 88% accuracy, while in the third stage, it achieved 86% accuracy for the “Poor Outcome” group. By increasing accuracy across unbalanced classes and emphasizing its potential for practical use, the multi-stage technique presents a promising solution for predicting the severity of SAH. Future research will concentrate on additional tuning to improve the model’s efficacy in actual healthcare environments.</p><h2>Other Information</h2><p dir="ltr">Published in: Neurosurgical Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10143-025-03678-9" target="_blank">https://dx.doi.org/10.1007/s10143-025-03678-9</a></p>2025-07-10T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10143-025-03678-9https://figshare.com/articles/journal_contribution/Multi-class_subarachnoid_hemorrhage_severity_prediction_addressing_challenges_in_predicting_rare_outcomes/30542501CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305425012025-07-10T09:00:00Z |
| spellingShingle | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes Muhammad Mohsin Khan (22150360) Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Subarachnoid hemorrhage Severity prediction Modified rankin scale Imbalanced data Electronic health record |
| status_str | publishedVersion |
| title | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes |
| title_full | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes |
| title_fullStr | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes |
| title_full_unstemmed | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes |
| title_short | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes |
| title_sort | Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes |
| topic | Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Subarachnoid hemorrhage Severity prediction Modified rankin scale Imbalanced data Electronic health record |