Heart Stroke Risk Prediction Using Machine Learning and Deep Learning Algorithm

Heart attack is a catch-all term for a variety of conditions affecting the heart. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. This paper aims to develop an Intelligent System using various ML techniques such as Naive Bayes, KNN, Random Forest and Decision Tree. Users of this web-based software will answer a prepared series of questions. Data analytics is crucial for organizing, analyzing, and controlling large data sets. It has shown beneficial in the diagnosis, prevention, and treatment of cardiovascular disease. With this in mind, we want to apply SVM- and GA-based data analytics to the challenge of identifying cardiac problems. A comparison of these algorithms’ prediction models indicates which one is the most developed. It may aid physicians in making more educated diagnoses of heart illness than would have been possible with older, more traditional methods of decision assistance. By providing efficient treatments, it contributes to cost-effective healthcare.