Explainable Multimodal Ai Framework for Early Cardio vascular Disease Prediction Using Clinical Data and ECG Signals

Abstract

Worldwide, cardiovascular disease (CVD) is one of the major causes of death and having a reliable and timely prediction system would help address this. This study suggests an Explainable Multimodal Artificial Intelligence (AI) system that combines clinical parameters and electrocardiogram (ECG) signals for early detection of cardiovascular disease. The framework utilizes cutting-edge data science methods, such as deep learning, to combine data from diverse sources and create more holistic insights into patient health. Clinical variables like age, blood pressure, cholesterol, and so on, are fed into the neural network together with the ECG signal representations, using specialized neural network architectures. A multimodal fusion scheme is used to fuse the learned features and build a more reliable diagnosis with more prediction power. There is a need for “black boxes” in the case of deep learning models, so explainable AI (XAI) techniques are also integrated to make the results of prediction understandable. The model allows clinicians and healthcare professionals to determine the most influential factors that benefit cardiovascular risk predictions, where increasing trust and usability are all important. Experimental results show great improvements in prediction accuracy when combining clinical and ECG data in comparison to single modality. Moreover, the model explains part, giving useful insights into the process of model decisions, without compromising the prediction efficacy. The suggested solution is effective and interpretable and can be implemented in data-driven healthcare systems and adopted for medical prediction tasks with the application of explainable deep learning.