A Hybrid AI Model for Early Detection of Heart Disease Using Clinical and ECG Data

الملخص

One of the main causes of morbidity and death is still cardiovascular diseases (CVDs) thereby stressing the need for developing effective early diagnostic tools. This paper proposes a hybrid approach using AI that combines deep ECG feature learning with clinical data to enhance early heart disease diagnosis. The ECG signals were preprocessed, divided into 2-second epochs, and then represented as 128-dimensional embedding using a 1D CNN-based feature extractor. At the same time, a set of 13 clinical risk factors was normalized and used to construct a lightweight clinical prediction branch. Both were then fused using a multimodal neural network that learns both ECG and clinical features. The findings of the experiment have demonstrated the effectiveness of the proposed hybrid framework, which has achieved 97.56% accuracy, 0.996 AUC, 99.0% sensitivity, and 96.0% specificity, significantly outperforming the individual models. Furthermore, the SHAP values-based explain ability method has assisted in revealing the most important clinical variables and ECG embedding features, thereby making the prediction results interpretable and meaningful from the clinical perspective. In summary, the proposed approach has shown how multimodal AI systems might help with early heart disease diagnosis and offers a promising solution for future real-time applications.