Predicting Functional Outcome After Ischemic Stroke Using Logistic Regression and Machine Learning Models

Abstract

This research employed binary logistic regression and machine learning techniques; Decision Tree, Random Forest, and Support Vector Machine (SVM), to predict functional outcomes following ischemic stroke. The main goal was to determine the most suitable model for the dataset through a comprehensive performance evaluation. Four models were examined for predicting post-ischemic stroke functional outcomes: Decision Tree, Random Forest, Logistic Regression, and SVM. The evaluation involved metrics such as Accuracy, Precision, F1-Score, and Recall. The Logistic Regression model achieved the highest accuracy at 90%, accurately predicting outcomes in 90% of cases. However, it had lower precision (50%), indicating an increased rate of false positive predictions. On the other hand, the SVM model displayed the highest precision (71.3%), implying fewer false positive predictions. It also attained the highest F1-Score (77.5%), indicating a strong balance between precision and Recall compared to the other models. Notably, the Logistic Regression model achieved perfect Recall (100%), correctly identifying all positive outcomes, while the Random Forest model showed significant recall performance (93.2%). Conversely, the Decision Tree model exhibited moderate accuracy (66.11%) but lower precision (66%), F1-Score (6.15%), and recall (3.2%), suggesting challenges with false positives and false negatives. Choosing the best model depends on analysis priorities. For accurate identification of positive outcomes, the Logistic Regression model's perfect recall is advantageous. For balanced performance, the SVM model's high F1-Score makes it a compelling option.