Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning
Abstract Aneurysmal subarachnoid haemorrhage (aSAH) is a type of stroke with high mortality and morbidity. This study aimed to identify novel aSAH risk factors by combining machine learning (ML) and traditional statistical methods. Using the UK Biobank, we identified aSAH cases via hospital-based IC...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-88826-3 |
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| author | Jos P. Kanning Junfeng Wang Shahab Abtahi Mirjam I. Geerlings Ynte M. Ruigrok |
| author_facet | Jos P. Kanning Junfeng Wang Shahab Abtahi Mirjam I. Geerlings Ynte M. Ruigrok |
| author_sort | Jos P. Kanning |
| collection | DOAJ |
| description | Abstract Aneurysmal subarachnoid haemorrhage (aSAH) is a type of stroke with high mortality and morbidity. This study aimed to identify novel aSAH risk factors by combining machine learning (ML) and traditional statistical methods. Using the UK Biobank, we identified aSAH cases via hospital-based ICD codes and analysed 618 baseline variables covering demographics, lifestyle, medical history, and physical measurements. The CatBoost ML algorithm and Shapley Additive Explanations (SHAP) identified the top 25 variables most influential in predicting aSAH. Logistic regression further described these variables while adjusting for established aSAH risk factors. Among 501,847 participants, 893 aSAH cases were identified. ML identified 214 variables with non-zero SHAP values. Logistic regression of the top 25 variables revealed four potential novel aSAH risk factors. Increased aSAH risk was associated with mean sphered cell volume (OR 1.02, 95% CI 1.00-1.03) and tea intake (OR 1.03, 95% CI 1.01–1.05). Decreased aSAH risk was associated with peak expiratory flow (OR 0.80, 95% CI 0.66–0.96), and haematocrit percentage (OR 0.97, 95% CI 0.95-1.00). Future research should validate these findings and explore the potential non-linear relationships and interactions indicated by the ML models. |
| format | Article |
| id | doaj-art-1b4f8a33dd324159af2f9e1870c50f45 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-1b4f8a33dd324159af2f9e1870c50f452025-08-20T02:41:33ZengNature PortfolioScientific Reports2045-23222025-03-011511910.1038/s41598-025-88826-3Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learningJos P. Kanning0Junfeng Wang1Shahab Abtahi2Mirjam I. Geerlings3Ynte M. Ruigrok4Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center UtrechtDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht UniversityDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht UniversityJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityDepartment of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center UtrechtAbstract Aneurysmal subarachnoid haemorrhage (aSAH) is a type of stroke with high mortality and morbidity. This study aimed to identify novel aSAH risk factors by combining machine learning (ML) and traditional statistical methods. Using the UK Biobank, we identified aSAH cases via hospital-based ICD codes and analysed 618 baseline variables covering demographics, lifestyle, medical history, and physical measurements. The CatBoost ML algorithm and Shapley Additive Explanations (SHAP) identified the top 25 variables most influential in predicting aSAH. Logistic regression further described these variables while adjusting for established aSAH risk factors. Among 501,847 participants, 893 aSAH cases were identified. ML identified 214 variables with non-zero SHAP values. Logistic regression of the top 25 variables revealed four potential novel aSAH risk factors. Increased aSAH risk was associated with mean sphered cell volume (OR 1.02, 95% CI 1.00-1.03) and tea intake (OR 1.03, 95% CI 1.01–1.05). Decreased aSAH risk was associated with peak expiratory flow (OR 0.80, 95% CI 0.66–0.96), and haematocrit percentage (OR 0.97, 95% CI 0.95-1.00). Future research should validate these findings and explore the potential non-linear relationships and interactions indicated by the ML models.https://doi.org/10.1038/s41598-025-88826-3 |
| spellingShingle | Jos P. Kanning Junfeng Wang Shahab Abtahi Mirjam I. Geerlings Ynte M. Ruigrok Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning Scientific Reports |
| title | Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning |
| title_full | Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning |
| title_fullStr | Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning |
| title_full_unstemmed | Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning |
| title_short | Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning |
| title_sort | identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning |
| url | https://doi.org/10.1038/s41598-025-88826-3 |
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