Optimizing Stroke Detection: An Analysis of Different Feature Selection Approaches

Stroke is a serious condition that can make it impossible for some body parts to move. When blood flow to a portion of the brain is suddenly cut off, a stroke results. A stroke patient needs to receive treatment right away because delaying care could seriously harm their health. This research aims to identify the optimal model with important features based on several machine learning techniques that can predict the risk of stroke from the provided data. The combined dataset of publicly available data with 733 collected data is used to train the model. We have analyzed the result of 4 different feature selection techniques - Information gain, Lasso L1, Fisher score and Kendall's Tau, to reduce dimensionality and ensure a cost-effective and scalable stroke detection system. Also, 5-fold cross-validation with Grid Search CV hyperparameter tuning is used to remove any potential overfitting issues. In addition, we have evaluated the model using the Brier score, G-mean, Matthews Correlation Coefficient (MCC), and H-measure. Lasso L1 emerges as the most compelling feature selection technique for this project. With this technique, the best accuracy of 90%, H-measure of 89.9% and AUC score of 96% are provided by Gradient Boosting among the eight machine learning algorithms we have used.