A Deep Learning Approach for ECG-Based Cardiac Anomaly Detection with Improved Feature Extraction

Heart disease is a global health concern that requires accurate and timely prediction tools for successful management and treatment. Electrocardiogram (ECG) data analysis helps discover cardiac anomalies by revealing heart function and risk factors. ECG data is complicated and variable, making interpretation and prediction difficult. Researchers have used preprocessing, feature extraction, and machine learning algorithms to enhance heart disease prediction. Ensemble learning can improve predictive performance by combining model predictions. This study attempts to improve cardiac risk assessment and patient outcomes by merging multiple techniques.The goal of this paper is to improve heart disease prediction accuracy by evaluating ECG data using machine learning techniques. To improve cardiac abnormality identification, preprocess ECG images, extract key characteristics, and use ensemble learning. The work investigates signal contour extraction and dimensionality reduction to provide a comprehensive cardiac risk assessment algorithm.The study uses a multi-step technique to improve heart disease prediction using ECG data. Preprocessing ECG pictures with signal contour extraction, grayscale conversion, and Gaussian blurring improves data quality and clarity. To find predictive modelling features, feature extraction methods like PCA dimensionality reduction are used. Ensemble learning methods like the Voting Classifier combine predictions from numerous machine learning models to increase predictive performance. Hyperparameter adjustment improves model performance, achieving 98% testing set correctness. This study's methods give a complete foundation for precise and fast cardiac risk assessment.