Machine Learning based approach for Myocardial Infarction Prediction using real time ECG Signals

This project focuses on utilizing machine learning techniques to identify and detect abnormal cardiac conditions through the analysis of Electrocardiogram (ECG) signals. With cardiovascular diseases being a significant global health concern, the need for precise diagnostic tools is paramount. The primary objectives of this study involve identifying specific cardiac conditions, including Myocardial Infarction, Arrhythmias and discerning normal cardiac conditions. Real time ECG signals were taken from Symbiosis hospital and research center pvt. Limited hospital in Satara. The methodology encompasses the acquisition of diverse ECG datasets, preprocessing to enhance signal quality, and feature extraction to characterize different cardiac conditions effectively. Support vector machines (SVMs), logistic regression, kNNs, and XGBoost these machine learning techniques are employed to develop robust classification models. The anticipated outcomes of this research aim to provide accurate models capable of early detection, enabling prompt interventions and, ultimately, enhancing patient outcomes within the domain of cardiovascular health.