A Hybrid Approach Using SVM, kNN and Random Forest for ECG Classification

A concerned health problem that has to be taken care of which is causing illness and death is cardiac arrhythmia. Consequently, the use of effective tools to predict and diagnose this condition is highly desirable. Arrhythmia prediction is the main focus of this research, which examines three main content areas: machine learning model selection, electrocardiogram (ECG) feature selection, and model performance evaluation. The goal is to formulate machine learning boundaries that can accurately use ECG signals to predict simple heart arrhythmias. The potential of this study to improve clinical practice and patient outcomes and to guide future research in this area is of great importance and is essential for the development of accurate tools for the diagnosis of cardiomyopathy using ECG features a most effective identification and predictive machine learning models, and can help formulate treatment options.