Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population
Abstract Machine learning (ML) has made some significant contributions to stroke prevention, but the stability and accuracy of existing models for clinical applications are uncertain. This study develops and validates an interpretable ML model using metabolic and coagulation biomarkers to predict is...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Online Access: | https://doi.org/10.1002/VIW.20240059 |
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| author | Xiaoyue Lyu Jie Liu Yingying Gou Shengli Sun Jing Hao Yali Cui |
| author_facet | Xiaoyue Lyu Jie Liu Yingying Gou Shengli Sun Jing Hao Yali Cui |
| author_sort | Xiaoyue Lyu |
| collection | DOAJ |
| description | Abstract Machine learning (ML) has made some significant contributions to stroke prevention, but the stability and accuracy of existing models for clinical applications are uncertain. This study develops and validates an interpretable ML model using metabolic and coagulation biomarkers to predict ischemic stroke in elderly hypertensive patients in Northwest China. The prediction model used 453 electronic medical records for the model building (80% as a training set and 20% as a test set) and 132 for external validation. The final seven key features (D‐dimer, cystatin C, homocysteine, hemoglobin A1c, prothrombin time, low‐density lipoprotein C, and triglyceride glucose‐body mass index) were selected by the advanced approach, elastic net, and classical wrapping approaches. The final model, eXtreme gradient boosting, was identified as having superior performance than the other 9 classifers (random forest, Gaussian process, multilayer perceptron, logistic regression, support vector machine, K‐nearest neighbor, decision tree, Gaussian naive bayes, and ensemble model), with area under the receiver‐operating characteristic curves of 0.97 and 0.94 for the test and external validation sets, respectively. The final model demonstrates excellent stability, accuracy, and clinical usefulness through various metrics and decision curve analysis. Additionally, an online human–machine interface application has been developed for clinical practice to help early identification and intervention for ischemic stroke in elderly hypertensive patients. |
| format | Article |
| id | doaj-art-89e34ac480774f8da24a347af82c792e |
| institution | OA Journals |
| issn | 2688-3988 2688-268X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | View |
| spelling | doaj-art-89e34ac480774f8da24a347af82c792e2025-08-20T01:58:04ZengWileyView2688-39882688-268X2024-12-0156n/an/a10.1002/VIW.20240059Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive populationXiaoyue Lyu0Jie Liu1Yingying Gou2Shengli Sun3Jing Hao4Yali Cui5Faculty of Life Sciences and Medicine Northwest University Xi'an People's Republic of ChinaThe School of Automation Northwestern Polytechnical University Xi'an People's Republic of ChinaFaculty of Life Sciences and Medicine Northwest University Xi'an People's Republic of ChinaDepartment of Neurology Northwest University Affiliated Shenmu Hospital Shenmu People's Republic of ChinaFaculty of Life Sciences and Medicine Northwest University Xi'an People's Republic of ChinaFaculty of Life Sciences and Medicine Northwest University Xi'an People's Republic of ChinaAbstract Machine learning (ML) has made some significant contributions to stroke prevention, but the stability and accuracy of existing models for clinical applications are uncertain. This study develops and validates an interpretable ML model using metabolic and coagulation biomarkers to predict ischemic stroke in elderly hypertensive patients in Northwest China. The prediction model used 453 electronic medical records for the model building (80% as a training set and 20% as a test set) and 132 for external validation. The final seven key features (D‐dimer, cystatin C, homocysteine, hemoglobin A1c, prothrombin time, low‐density lipoprotein C, and triglyceride glucose‐body mass index) were selected by the advanced approach, elastic net, and classical wrapping approaches. The final model, eXtreme gradient boosting, was identified as having superior performance than the other 9 classifers (random forest, Gaussian process, multilayer perceptron, logistic regression, support vector machine, K‐nearest neighbor, decision tree, Gaussian naive bayes, and ensemble model), with area under the receiver‐operating characteristic curves of 0.97 and 0.94 for the test and external validation sets, respectively. The final model demonstrates excellent stability, accuracy, and clinical usefulness through various metrics and decision curve analysis. Additionally, an online human–machine interface application has been developed for clinical practice to help early identification and intervention for ischemic stroke in elderly hypertensive patients.https://doi.org/10.1002/VIW.20240059hypertensionischemic strokemachine learningrisk model |
| spellingShingle | Xiaoyue Lyu Jie Liu Yingying Gou Shengli Sun Jing Hao Yali Cui Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population View hypertension ischemic stroke machine learning risk model |
| title | Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population |
| title_full | Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population |
| title_fullStr | Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population |
| title_full_unstemmed | Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population |
| title_short | Development and validation of a machine learning‐based model of ischemic stroke risk in the Chinese elderly hypertensive population |
| title_sort | development and validation of a machine learning based model of ischemic stroke risk in the chinese elderly hypertensive population |
| topic | hypertension ischemic stroke machine learning risk model |
| url | https://doi.org/10.1002/VIW.20240059 |
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