An Effective Framework for Predicting Stroke Prediction using Machine Learning Technique

Today, adequately trained machine learning algorithms can be significantly used in fields such as surveillance, medicine, and data management to identify and provide solutions to problems that do not have an answer or if the current solutions are ineffective. A stroke is when blood arteries in the brain burst, harming the brain. It may also occur if the brain's supply of nutrients and blood is interrupted. The severity of a stroke can be lessened by early recognition of numerous warning symptoms. Our lifestyle has recently changed, especially in the covid era. Nowadays, Millions of people encounter illnesses, disabilities, cardiovascular diseases, being overweight, hypertension, and many more. There are more than 10 million cases per year in India only. When something stops the blood cells' flow, a person encounters a heart attack. Because of this, the tissue will start to die because of a lack of blood supply. So, we proposed a machine learning model for early detection. So, we proposed a machine learning model for early detection. The dataset used is from Kaggle, with various parameters that have been considered. In this paper, we have developed several machine learning algorithms for the early detection of the disease, taking into consideration multiple risk factors and physiological parameters that have caused the stroke. We trained our model using many machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Neural Network (NN), and then we checked its testing accuracy. We found the highest accuracy in the XGBoost Classifier with 100% accuracy and 99% accuracy in the Random Forest Classifier algorithm. We also used ensemble learning and successfully got 100% testing accuracy.