Leveraging Cloud-Based Deep Learning for Cardiovascular Diseases Prediction: Challenges and Solutions

Cardiovascular diseases CVDs are a growing threat to global mortality therefore the need for accurate early prediction of diseases is imperative to improve patient outcome. The cloud-based deep learning model provides powerful CVD prediction solutions using large volumes of multi-structured medical data. But even then there are considerable challenges regarding data privacy, model interpretability, and effectiveness which inhibit effective application in clinics. The objective of this paper is to evaluate the role of explainable AI in the prediction of cardiovascular diseases using cloud based deep learning. Important shortcomings such as lack of model interpretability and trust are also discussed along with solutions to counter them. Here, XAI methods, for example SHAP and LIME may be used to enhance model interpretability. Clarifying the basis for AI predictions can enhance clinician's trust in AI-based decision making systems. Utilizing data augmentation techniques, multi-modal data integration, transfer learning, and other approaches can overcome data challenges and improve prediction outcomes. The paper further addresses the issues of data confidentiality and time multi-structured prediction by exploring federated learning and hybrid cloud models. This paper offers a new perspective in building XAI enhanced cardiovascular disease (CVD) predictive Deep Learning models through the integration of interpretability techniques and cloud infrastructure that will ultimately lead to better results, greater access to the systems, all while allowing the use of more personalized and effective healthcare methods.