Heart and Cardiac Arrest Analysis by Prediction of Hybrid Model

The study is primarily concerned in predicting cardiovascular illness in the actual world. The prediction of heart disease is influenced by a number of risk factors. AI (ML) is demonstrating its ability to assist in decision-making based on the vast quantity of data offered by the medical services business. Various tests offer a quick look at predicting heart infection using ML procedures. In this work, we offer a new way for identifying key traits using AI techniques to increase cardiovascular expectation accuracy. The early model includes several highlights as well as a number of well-known categorization algorithms. By using a hybrid random forest linear model that combines two separate methods to predict cardiovascular disease. With an accuracy level upto 88.2, we are providing the better prediction. The associated data set which is finally produced was obtained from the well-known University of California, Irvine’s repository. The HRFLM algorithm presented will aid doctors in diagnosing cardiac patients.