Deep Learning-Powered Cardiovascular Disease Prediction: A Novel Approach to Early Diagnosis and Risk Assessment
DOI:
https://doi.org/10.52783/jns.v14.2421Keywords:
Deep Learning, Cardiovascular Disease Prediction, AI in Healthcare, Risk Stratification, Medical Imaging, Neural Networks, Explainable AI, Electronic Health Records, Precision Cardiology, Clinical Decision SupportAbstract
Cardiovascular disease (CVD) continues to be a leading cause of death and disability worldwide, underscoring the critical need for improved risk prediction and early diagnosis. Traditional risk models, such as the Framingham Risk Score, provide valuable insights but are limited in their ability to incorporate the diverse, multi-dimensional data necessary for personalized healthcare. In response to this challenge, we propose a novel deep learning-based framework that integrates clinical, genetic, and imaging data to enhance CVD prediction and risk stratification.
The proposed model utilizes Convolutional Neural Networks (CNNs) for analyzing cardiovascular imaging and Recurrent Neural Networks (RNNs)/Long Short-Term Memory (LSTM) for processing sequential data from electronic health records (EHRs). By employing attention mechanisms, the model effectively combines these diverse data types to provide a more comprehensive evaluation of risk factors. The model was trained on large-scale datasets, including MIMIC-III and UK Biobank, and transfer learning techniques were applied to improve generalizability across various patient populations. Additionally, we incorporate Explainable AI (XAI) tools, such as SHAP and Grad-CAM, to facilitate clinical interpretability, enabling healthcare professionals to understand and trust the model’s predictions.
Experimental results demonstrate that our deep learning framework significantly outperforms traditional machine learning models, achieving higher accuracy, sensitivity, and specificity in predicting the onset of CVD. Furthermore, the model shows robust generalizability across diverse demographic groups and offers real-time monitoring potential through integration with wearable devices. To ensure data privacy, we introduce federated learning, allowing the model to train across multiple institutions without sharing sensitive patient data.
This study represents a significant advancement in the field of AI-driven precision cardiology, providing a scalable solution for early detection, personalized treatment, and clinical decision support. Future work will focus on refining model generalization, incorporating real-time data from wearables, and addressing regulatory and ethical considerations to promote widespread adoption.
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