Deep Learning for ECG Anomaly Detection: A Robust Real-Time Solution for Cancer Prediction

Early and accurate detection of cancer-related anomalies in electrocardiograms (ECG) can significantly improve patient outcomes. This paper presents a deep learning-based ECG anomaly detection system designed for the real-time identification of breast cancer and brain tumors. By integrating convolutional neural networks (CNNs) with recurrent neural networks (RNNs), transformer-based attention, ensemble learning, and additional strategies to address data imbalance and domain adaptation, we classify subtle ECG patterns that could indicate latent malignancies. Publicly available datasets—including the MIT-BIH Arrhythmia Database and PhysioNet’s PTB-XL are used to train, evaluate, and validate the system’s real-time capabilities. Experimental results reveal superior performance compared to traditional analytical approaches and underscore the feasibility of AI-driven ECG-based oncology diagnostics. These findings pave the way for an automated, non-invasive screening tool that can be seamlessly integrated into clinical workflows, delivering timely and accurate alerts for at-risk individuals.