Loading your page…
Preprints.org
By Shuvo TA and Hossain K.
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.
Ward brings these articles into the same app as your cards and questions. One free account for Ward, Suture, Osler and Prognia.
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.
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%.
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.
This is a preprint: it has not been peer reviewed yet, and its findings may change.
Published in Preprints.org (preprint). The abstract is shown as published and remains the work of its authors; read the full article at the source.
Prognia lists this article for reference and education. It is not medical advice. Always read the original and follow current guidelines and local protocols before acting on it.
