Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression
BackgroundIschemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment t...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1522868/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850074980905123840 |
|---|---|
| author | Kun Guo Kun Guo Bo Zhu Lei Zha Yuan Shao Zhiqin Liu Naibing Gu Kongbo Chen |
| author_facet | Kun Guo Kun Guo Bo Zhu Lei Zha Yuan Shao Zhiqin Liu Naibing Gu Kongbo Chen |
| author_sort | Kun Guo |
| collection | DOAJ |
| description | BackgroundIschemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment tools. Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.ObjectiveThe aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.MethodsA retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. The dataset included demographic information, medical histories, clinical evaluations, and laboratory results. Six ML models were constructed: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, XGBoost, and AdaBoost. Model performance was evaluated using the Area Under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, predictive values, and F1 score, with five-fold cross-validation to ensure robustness.ResultsThe training set, identified key variables associated with stroke prognosis, including hypertension, diabetes, and smoking history. The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. Our models showed high predictive accuracy and stability across both datasets.ConclusionThis study presents a robust ML approach for predicting stroke prognosis, with the SVM model and Nomogram providing valuable tools for clinical decision-making. By incorporating advanced ML techniques, we enhance the precision of prognostic assessments and offer a theoretical and practical framework for clinical application. |
| format | Article |
| id | doaj-art-58d4743597b54ac6b7bd2f1c04aa34dd |
| institution | DOAJ |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-58d4743597b54ac6b7bd2f1c04aa34dd2025-08-20T02:46:25ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-03-011610.3389/fneur.2025.15228681522868Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regressionKun Guo0Kun Guo1Bo Zhu2Lei Zha3Yuan Shao4Zhiqin Liu5Naibing Gu6Kongbo Chen7Xi’an Central Hospital, Xi’an, ChinaTongchuan Mining Bureau Central Hospital, Tongchuan, ChinaXi’an Central Hospital, Xi’an, ChinaXi’an Central Hospital, Xi’an, ChinaTongchuan Mining Bureau Central Hospital, Tongchuan, ChinaXi’an Central Hospital, Xi’an, ChinaXi’an Central Hospital, Xi’an, ChinaTongchuan Mining Bureau Central Hospital, Tongchuan, ChinaBackgroundIschemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment tools. Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.ObjectiveThe aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.MethodsA retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. The dataset included demographic information, medical histories, clinical evaluations, and laboratory results. Six ML models were constructed: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, XGBoost, and AdaBoost. Model performance was evaluated using the Area Under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, predictive values, and F1 score, with five-fold cross-validation to ensure robustness.ResultsThe training set, identified key variables associated with stroke prognosis, including hypertension, diabetes, and smoking history. The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. Our models showed high predictive accuracy and stability across both datasets.ConclusionThis study presents a robust ML approach for predicting stroke prognosis, with the SVM model and Nomogram providing valuable tools for clinical decision-making. By incorporating advanced ML techniques, we enhance the precision of prognostic assessments and offer a theoretical and practical framework for clinical application.https://www.frontiersin.org/articles/10.3389/fneur.2025.1522868/fullischemic strokemachine learningprognosispredictive modelingclinical decision support |
| spellingShingle | Kun Guo Kun Guo Bo Zhu Lei Zha Yuan Shao Zhiqin Liu Naibing Gu Kongbo Chen Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression Frontiers in Neurology ischemic stroke machine learning prognosis predictive modeling clinical decision support |
| title | Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression |
| title_full | Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression |
| title_fullStr | Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression |
| title_full_unstemmed | Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression |
| title_short | Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression |
| title_sort | interpretable prediction of stroke prognosis shap for svm and nomogram for logistic regression |
| topic | ischemic stroke machine learning prognosis predictive modeling clinical decision support |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1522868/full |
| work_keys_str_mv | AT kunguo interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression AT kunguo interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression AT bozhu interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression AT leizha interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression AT yuanshao interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression AT zhiqinliu interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression AT naibinggu interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression AT kongbochen interpretablepredictionofstrokeprognosisshapforsvmandnomogramforlogisticregression |