Cardiac Disease Prediction for Heart Monitoring using Data Mining Techniques

There are millions or tonnes of data relating to the healthcare and medical sectors, and these data hold a wealth of hidden information. Authors can use this knowledge to their advantage in their connected industries by using it to inform their judgments. There are several highly sophisticated methods and approaches for mining and making effective judgments with the information that has been successfully collected. Coherence Cardiac Risk Prediction System (CCRPS), a highly successful system for the early detection or forecasting of different levels of cardiac disease risk, was created utilizing neural networks. The training approach used in this work is a multilayer back propagation neural network for perception. The study's objective is to apply artificial intelligence (AI) to the diagnosis of heart illness in both pathological and normal contexts. Data mining-based approaches are commonly used, and many different AI technologies are anticipated to be used. The investigation's conclusions show that the UCI Datasets have an accuracy of 99.77 percent.