Application of Machine Learning Techniques for Predicting Stroke Disease

Stroke is a cerebrovascular illness caused by a sudden halt in blood flow to the brain, resulting in neurological impairment. Stroke is a major public health problem worldwide, affecting millions of people. It is a significant source of illness and mortality, imposing a significant socio-economic burden. A thorough awareness of the current global situation is required for effective treatments and preventive actions. This research compares data mining techniques for the prediction of stroke illness. Using a dataset obtained from Mayo Hospital, Lahore, that had 2326 instances, each with 11 attributes, we compared the performance of Support Vector Machine (SVM), Random Forest, Neural Network, and K-Nearest Neighbors (KNN) approaches. Orange Data Mining Software was applied to evaluate the data and execute machine learning techniques. The results show that Naïve Bayes is the best method for predicting the prevalence of Stroke disease. The proposed model demonstrates an Area Under the Curve (AUC) of 88.3 %, an accuracy of 80.8%, and notable metrics including an F1-Score and precision.