Improving Cardiovascular Stroke Risk Prediction with Machine Learning

Cardiovascular diseases have overtaken all other causes of death in industrialized, developing, and underdeveloped countries over the previous several decades. Receiving clinical care and detecting heart abnormalities early can help to reduce death rates. We created a model for forecasting heart illness and detecting imminent heart disease using machine learning techniques such as logistic regression, SVM, Multinomial Nave Bayes, Random Forest, and Decision Tree based on the patient's various cardiac data. The most common type of input is numerical data representing a variety of parameters. Real-time output results are then generated to determine whether or not the patient has a disease. Before deciding on the best supervised machine learning technique for the model, we will try a variety of approaches. Current systems use imprecise and inefficient traditional deep learning models. They take slightly longer to process and are less precise than the suggested model.