Abstract

A big issue in the world of medical computing or clinical care is the classification of large medical records in particular cases of cardiac disease. Lack of a proper method for diagnosing cardiovascular disease results in a lack of early prediction. By designing machine-learning algorithms, a greater provision can be made for classifying patients on the basis of clinical records in the prediction of cardiovascular disease. In this article, we use a machine learning model to forecast cardiac rate at an earlier rate that enhances exam and assessment precision. This approach covers both cardiovascular disease surveillance, classification and estimation on a large dataset in real time. The experimental findings demonstrate the reliability of the proposed approach in real time datasets against existing methods and increase the precision in classification.