Our research intends to extensively investigate the capabilities of three distinct machine learning algorithms, namely Random Forest, K-Nearest Neighbours, and Logistic Regression. Our key goal is to assess their ability to foresee outcomes and comprehend them easily. We intend to acquire significant insights into the potential of these three strategies and their applicability in diverse fields by undertaking a complete analysis. We used 188 ECG interpretations from 19566 patients, which included various sorts of data. We evaluated the data with various quantities of data ranging from 165 to 520 training sets. Algorithms based on K-Nearest Neighbour (KNN), Logistic Regression (LR), and Random Forest each achieved 98%, 94%, and 99% accuracy, respectively.

Random Forest, K-Nearest Neighbors, Logistic regression, Electrocardiogram