Machine Learning-Based Approach for ECG Analysis and Cardiac Anomaly Detection to Enhance Early Diagnosis and Treatment

Cardiovascular diseases are still the most common cause of death in the world, so early detection methods need to get better. This study paper describes a complete machine learning-based method for analysing Electrocardiograms (ECGs) with the goal of making heart abnormal identification more accurate and faster. We used different ECG recordings as a dataset and a number of strong machine learning classifiers, such as Support Vector Machines (SVM), Random Forests, K-Nearest Neighbours (KNN), XGBoost, and LightGBM, along with new Autoencoders, to effectively find and label ECG abnormalities. The proposed method, ECG data are first normalised and noise disturbance is reduced. Next, features are extracted to find important diagnostic signs like changes in the QRS complex, PR gap, and ST segment. The models were trained and tested using these features, and the goal was to get the best detection accuracy by tweaking each model's parameters. Autoencoders were also used to pick up complex, nonlinear patterns in the ECG data. This made it easier to find small problems that older methods might miss. Findings show that while standard algorithms like SVM and Random Forests were pretty good at predicting the future, group methods like XGBoost and LightGBM were much better. It was KNN that helped prove the stability across data that hadn't been seen before. It's worth mentioning that the Autoencoders were very good at finding anomalies, especially when the ECG data was noisy. Our combined method greatly increased the number of early diagnoses, which suggests that it could be used in real life in systems that watch people's health all the time. The combining several machine learning methods for better heart health monitoring works really well, as shown in this study. This could be a useful tool for doctors to find and treat cardiovascular problems early on.