Predicting stroke severity of patients using interpretable machine learning algorithms

Background Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes,...

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Published in:	European journal of medical research Vol. 29; no. 1; pp. 547 - 23
Main Authors:	Sorayaie Azar, Amir, Samimi, Tahereh, Tavassoli, Ghanbar, Naemi, Amin, Rahimi, Bahlol, Hadianfard, Zahra, Wiil, Uffe Kock, Nazarbaghi, Surena, Bagherzadeh Mohasefi, Jamshid, Lotfnezhad Afshar, Hadi
Format:	Journal Article
Language:	English
Published:	London BioMed Central Ltd 14-11-2024 BioMed Central BMC
Subjects:	Algorithms Computational linguistics Data mining Interpretable machine learning Language processing Machine learning Medical research Medicine, Experimental Natural language interfaces Neural networks Prediction Rankings Stroke (Disease) Stroke severity Iran
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Summary:	Background Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes, with standard assessment tools being the Rapid Arterial Occlusion Evaluation (RACE) and the National Institutes of Health Stroke Scale (NIHSS). This study aims to utilize Machine Learning (ML) algorithms to predict stroke severity using these two distinct scales. Methods We conducted this study using two datasets collected from hospitals in Urmia, Iran, corresponding to stroke severity assessments based on RACE and NIHSS. Seven ML algorithms were applied, including K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Hyperparameter tuning was performed using grid search to optimize model performance, and SHapley Additive Explanations (SHAP) were used to interpret the contribution of individual features. Results Among the models, the RF achieved the highest performance, with accuracies of 92.68% for the RACE dataset and 91.19% for the NIHSS dataset. The Area Under the Curve (AUC) was 92.02% and 97.86% for the RACE and NIHSS datasets, respectively. The SHAP analysis identified triglyceride levels, length of hospital stay, and age as critical predictors of stroke severity. Conclusions This study is the first to apply ML models to the RACE and NIHSS scales for predicting stroke severity. The use of SHAP enhances the interpretability of the models, increasing clinicians' trust in these ML algorithms. The best-performing ML model can be a valuable tool for assisting medical professionals in predicting stroke severity in clinical settings. Keywords: Stroke severity, Prediction, Machine learning, Interpretable machine learning
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2047-783X 0949-2321 2047-783X
DOI:	10.1186/s40001-024-02147-1