GitHub - YuluZheng/Machine-learning-driven-approach-triage-for-ischemic-stroke: This study used the selected predictors obtained from the results of routine blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction.

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This study used the selected predictors obtained from the results of routine blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction.

link.springer.com/article/10.1007/s13167-022-00283-4

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Name		Name	Last commit message	Last commit date
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95% CI for Sens & Speci.ipynb		95% CI for Sens & Speci.ipynb
AUC 95% CI.ipynb		AUC 95% CI.ipynb
Step 2-Evaluation Metrics.ipynb		Step 2-Evaluation Metrics.ipynb
Step 5- independent visualisation SHAP feature.ipynb		Step 5- independent visualisation SHAP feature.ipynb
Step 6-SHAP for classification.ipynb		Step 6-SHAP for classification.ipynb
Step3-Feature-Imporatnces.ipynb		Step3-Feature-Imporatnces.ipynb
Step4- Final updated SHAP for classification.ipynb		Step4- Final updated SHAP for classification.ipynb
Updated step1-LASSO for feature selection.ipynb		Updated step1-LASSO for feature selection.ipynb