Design and Implementation of Cardiac Arrhythmia Detection using Machine Learning Algorithm for Healthcare Application

This research paper presents a system for cardiac arrhythmia detection using the AD8232 integrated ECG sensor and machine learning algorithms. The AD8232 sensor offers high-quality ECG signal acquisition, while machine learning enables accurate classification of arrhythmias. The methodology involves for detecting cardiac arrhythmia includes data acquisition, preprocessing, feature extraction, and training of machine learning models. Performance evaluation using metrics such as accuracy, precision, recall, and F1 score confirms the system's effectiveness. The developed system provides a non-invasive and cost-effective solution for continuous monitoring and early detection of cardiac abnormalities, facilitating timely interventions and personalized treatment plans. This paper contributes to improved patient care and diagnostic accuracy in cardiac arrhythmia detection. Analysis of different machine learning algorithm like random forest, logistic Regression, and Gaussian NB are compared by calculating accuracy. Random forest algorithm gives the better accuracy of 92.59.these machine learning model effectively identifies the normal and abnormal ECG pattern for Cardiac Arrhythmia Detection.