Leveraging Cloud Computing And Big Data Analytics To Enhance Diagnostic Accuracy In Cardiovascular Diseases

Abstract

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, with early detection and personalized treatment being crucial for improving patient outcomes. Leveraging cloud computing and big data analytics can revolutionize the way CVDs are diagnosed and treated by enabling real-time processing of large-scale patient data from diverse sources, such as electronic health records, medical imaging, and wearable devices. Despite the advancements in diagnostic tools, traditional methods often suffer from limitations such as reliance on isolated data, delayed detection, human error, and generalized treatment protocols. To address these challenges, the proposed method utilizes a transformer-based attention framework integrated with cloud computing and big data analytics, providing an innovative approach for enhanced diagnostic accuracy. This framework uses a combination of data preprocessing via Z-score normalization and real-time data processing, allowing for dynamic and personalized treatment plans. The model’s ability to prioritize the most relevant health data results in more accurate predictions and recommendations. The results indicate that the proposed method increases diagnostic accuracy by 20% over traditional approaches, enabling earlier detection, more effective treatments, and better management of cardiovascular health, ultimately improving patient outcomes and reducing healthcare costs.