Cardiovascular Disease (CVD) is a substantial health burden and cause of death worldwide. The electrocardiogram (ECG) is an electro-physiological signal that contains a wide range of valuable information about the electrical activity of the heart. Classification of the electrocardiogram (ECG) signals plays a crucial role in clinical diagnosis of heart disease. Analyzing the Electrocardiogram (ECG) is the current method of disease detection. Unfortunately, finding professionals to examine a big volume of ECG data takes up excessive medical time and money. As a result, machine learning-based methods for recognising ECG characteristics have steadily gained traction. These traditional methods have some limitations, such as the need for manual feature recognition, complex models, and a long learning time. Our goal is to solve these problems and predict certain heart diseases by classifying ECG signals using machine learning.

• Our goal is to reduce the death percentage of heart diseases, Early recognition of heart diseases will help us to reduce it.

• Detect and classify cardiac arrhythmias of patients from the ECG signal.

• Utilizing machine learning and improve optimization algorithms in ECG classification.

• Perform better results than simillar systems.

Our proposed methods aim to recognize and classify ECG signals. The methods used will help in effectively evaluating heart health easily. The methods are:

• Collect an ECG dataset and start data preprocessing to eliminate any inconsistent or duplicate data.

• Extract features that help to reduce the time and computation for the classification.

• work on several machine learning and deep learning approaches to reach better performance.

• Use metaheuristic optimization algorithms>2. Left bundle branch block

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