Machine Learning for Stroke Prediction: A Comparison of Scaling Techniques and Classification Algorithms

Background: Stroke prediction using machine learning is crucial for early detection, enabling timely interventions to reduce mortality and long-term disability. Our study aimed to find the best scaling method and classifier for stroke prediction. Methods: We applied three scaling techniques to the datasets: standardization, minmax, and robust scaling. For each scaling method, we employed eight classifiers: Gaussian Naïve Bayes, Decision Tree, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost), and Bagging Method. We used performance metrics such as accuracy, F1 score, recall, and precision to evaluate the model's performance. 10-fold cross-validation was conducted, and accuracy, precision, recall, and F1 score were assessed to determine the most robust classifier for the dataset. All analyses were performed using Python. Results: Across all the scaling techniques, XGBoost delivered remarkable performance. For standardization, min-max, and robust scaling, XGBoost achieved accuracies of 95.44%, 95.56%, and 95.39%, respectively. The Bagging and Random Forest Classifier also demonstrated strong accuracy across various scaling methods. Despite no statistical difference among the scaling methods, min-max scaling demonstrated a slight performance improvement. Under min-max scaling, the Bagging Classifier reached an accuracy of 93.32%, whereas the Random Forest Classifier attained an accuracy of 94.53%. Decision Tree 90.73%, SVM 83.70%, Logistic Regression 83.35%, and K-NN 88.02%. Conclusion: XGBoost achieved the highest accuracy, while min-max scaling enhanced the performance of all classifiers. These results highlight the importance of scaling and the effectiveness of XGBoost for stroke prediction.