Utilizing Enhanced Multiclass SVM for Predicting Atrial Fibrillation and Atrial Flutter in Cardiac Arrhythmias

Abstract:

Atrial flutter and atrial fibrillation are common cardiac arrhythmias characterised by abnormal heart rhythms originating in the atria. Atrial fibrillation involves chaotic and irregular atrial contractions, while atrial flutter features organised but rapid atrial rhythms. These disorders frequently arise from abnormalities in the heart's electrical system and are intricately connected to the general well-being of the heart. The use of machine learning (ML) techniques in recent days has significantly enhanced the ability to predict and diagnose these disorders. Patients with atrial fibrillation and atrial flutter face an increased risk of complications, including stroke and heart failure. Early identification and intervention are crucial for effective management. ML algorithms, utilising patient data as a key input, have proven to be valuable tools in predicting these disorders. Decision trees, support vector machines, and multiclass SVM classifiers, implemented through Python programming, contribute to the development of accurate prediction models. Performance assessments, focusing on accuracy, precision, and recall, facilitate a comprehensive comparative analysis of these models.  The recommended ML model not only aids in recognising undiagnosed cases but also provides a proactive approach to identifying individuals at a high risk of atrial fibrillation and atrial flutter. This application holds great promise for improving overall cardiovascular health assessment and facilitating timely and targeted medical interventions.