Cardiac Disease Diagnosis from Echocardiogram using Random Forest Classifier

Cardiovascular diseases (CVDs) represent a leading global health issue, necessitating early detection and accurate diagnosis for effective treatment. Electrocardiography (ECG) serves as an essential tool that provides valuable insights into the heart’s electrical activity, aiding in the identification of potential abnormalities. This project leverages machine learning techniques to enhance the diagnostic potential of ECG data, paving the way for improved cardiovascular care through precision medicine. The methodology focuses on training machine learning models to analyze ECG signals and classify heartbeats into categories that reflect various cardiac conditions. A com- prehensive dataset of approximately 20,000 ECG recordings, encompassing a wide range of heart rhythms and anomalies, establishes a robust foundation for model training. The Decision Tree classifier achieves an outstanding accuracy of 97%, demonstrating the ability to detect subtle patterns in ECG data. Furthermore, the Random Forest classifier showcases strong performance in distinguishing between different cardiac rhythms. Key metrics, such as precision, recall, and F1-score, provide a deeper understanding of the classifier’s diagnostic effectiveness. The confusion matrix serves as a visualization tool to pinpoint areas where model adjustments are necessary, further enhancing accuracy. These promising results highlight the potential of machine learning in transforming cardiac health monitoring and improving diagnostic processes, ultimately contributing to better patient care and outcomes. By harnessing advanced algorithms, this project illustrates the immense possibilities for integrating machine learning into modern healthcare systems.