Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine
IntroductionThe integration of traditional Chinese medicine (TCM) and Western medicine has demonstrated effectiveness in the primary prevention of stroke. Therefore, our study aims to utilize TCM syndromes alongside conventional risk factors as predictive variables to construct a machine learning mo...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1546878/full |
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| author | Liuding Wang Jingzi Shi Lina Miao Yifan Chen Jingjing Wei Min Jia Zhiyi Gong Ze Yang Jian Lyu Jian Lyu Jian Lyu Yunling Zhang Xiao Liang |
| author_facet | Liuding Wang Jingzi Shi Lina Miao Yifan Chen Jingjing Wei Min Jia Zhiyi Gong Ze Yang Jian Lyu Jian Lyu Jian Lyu Yunling Zhang Xiao Liang |
| author_sort | Liuding Wang |
| collection | DOAJ |
| description | IntroductionThe integration of traditional Chinese medicine (TCM) and Western medicine has demonstrated effectiveness in the primary prevention of stroke. Therefore, our study aims to utilize TCM syndromes alongside conventional risk factors as predictive variables to construct a machine learning model for assessing the risk of new-onset stroke.MethodsWe conducted a ten-year follow-up study encompassing 4,511 participants from multiple Chinese community hospitals. The dependent variable was the occurrence of the new-onset stroke, while independent variables included age, gender, systolic blood pressure (SBP), diabetes, blood lipids, carotid atherosclerosis, smoking status, and TCM syndromes. We developed the models using XGBoost in conjunction with SHapley Additive exPlanations (SHAP) for interpretability, and logistic regression with a nomogram for clinical application.ResultsA total of 1,783 individuals were included (1,248 in the training set and 535 in the validation set), with 110 patients diagnosed with new-onset stroke. The logistic model demonstrated an AUC of 0.746 (95% CI: 0.719–0.774) in the training set and 0.658 (95% CI: 0.572–0.745) in the validation set. The XGBoost model achieved a training set AUC of 0.811 (95% CI: 0.788–0.834) and a validation set AUC of 0.628 (95% CI: 0.537–0.719). SHAP analysis showed that elevated SBP, Fire syndrome in TCM, and carotid atherosclerosis were the three most important features for predicting the new-onset stroke.ConclusionUnder identical traditional risk factors, Chinese residents with Fire syndrome may have a higher risk of new-onset stroke. In high-risk populations for stroke, it is recommended to prioritize the screening and management of hypertension, Fire syndrome, and carotid atherosclerosis. However, future high-performance TCM predictive models require more objective and larger datasets for optimization. |
| format | Article |
| id | doaj-art-115fa93b0ac44798a906a9be8c3ece5c |
| institution | DOAJ |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-115fa93b0ac44798a906a9be8c3ece5c2025-08-20T03:01:14ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-02-011610.3389/fphar.2025.15468781546878Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicineLiuding Wang0Jingzi Shi1Lina Miao2Yifan Chen3Jingjing Wei4Min Jia5Zhiyi Gong6Ze Yang7Jian Lyu8Jian Lyu9Jian Lyu10Yunling Zhang11Xiao Liang12Departmalet of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaGraduate School, Beijing University of Chinese Medicine, Beijing, ChinaDepartmalet of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaDepartmalet of Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaDepartmalet of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaMedical Ethics Committee, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaShandong University of Traditional Chinese Medicine, Jinan, ChinaDepartmalet of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaDepartmalet of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaNMPA Key Laboratory for Clinical Research and Evaluation of Traditional Chinese Medicine, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaNational Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaDepartmalet of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaDepartmalet of Neurology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaIntroductionThe integration of traditional Chinese medicine (TCM) and Western medicine has demonstrated effectiveness in the primary prevention of stroke. Therefore, our study aims to utilize TCM syndromes alongside conventional risk factors as predictive variables to construct a machine learning model for assessing the risk of new-onset stroke.MethodsWe conducted a ten-year follow-up study encompassing 4,511 participants from multiple Chinese community hospitals. The dependent variable was the occurrence of the new-onset stroke, while independent variables included age, gender, systolic blood pressure (SBP), diabetes, blood lipids, carotid atherosclerosis, smoking status, and TCM syndromes. We developed the models using XGBoost in conjunction with SHapley Additive exPlanations (SHAP) for interpretability, and logistic regression with a nomogram for clinical application.ResultsA total of 1,783 individuals were included (1,248 in the training set and 535 in the validation set), with 110 patients diagnosed with new-onset stroke. The logistic model demonstrated an AUC of 0.746 (95% CI: 0.719–0.774) in the training set and 0.658 (95% CI: 0.572–0.745) in the validation set. The XGBoost model achieved a training set AUC of 0.811 (95% CI: 0.788–0.834) and a validation set AUC of 0.628 (95% CI: 0.537–0.719). SHAP analysis showed that elevated SBP, Fire syndrome in TCM, and carotid atherosclerosis were the three most important features for predicting the new-onset stroke.ConclusionUnder identical traditional risk factors, Chinese residents with Fire syndrome may have a higher risk of new-onset stroke. In high-risk populations for stroke, it is recommended to prioritize the screening and management of hypertension, Fire syndrome, and carotid atherosclerosis. However, future high-performance TCM predictive models require more objective and larger datasets for optimization.https://www.frontiersin.org/articles/10.3389/fphar.2025.1546878/fullartificial intelligencecombination of disease and syndromeprevention strategypopulations at high risk of stroketraditional medicine |
| spellingShingle | Liuding Wang Jingzi Shi Lina Miao Yifan Chen Jingjing Wei Min Jia Zhiyi Gong Ze Yang Jian Lyu Jian Lyu Jian Lyu Yunling Zhang Xiao Liang Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine Frontiers in Pharmacology artificial intelligence combination of disease and syndrome prevention strategy populations at high risk of stroke traditional medicine |
| title | Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine |
| title_full | Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine |
| title_fullStr | Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine |
| title_full_unstemmed | Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine |
| title_short | Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine |
| title_sort | predicting new onset stroke with machine learning development of a model integrating traditional chinese and western medicine |
| topic | artificial intelligence combination of disease and syndrome prevention strategy populations at high risk of stroke traditional medicine |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1546878/full |
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