Supervised machine learning methods for short-term prediction of a sudden cardiac death from electrocardiogram

Key Points

• Prediction algorithms identify markers of sudden cardiac death effectively using electrocardiogram data.
• A notable accuracy of 85% in identifying at-risk individuals was observed across multiple evaluation metrics.
• Data processing involved advanced supervised machine learning techniques to enhance predictive capability.
• The findings highlight the need for external validation to ensure reliability in real-world clinical settings.