Heart Disease Prediction using Secure Decentralized Data Sharing

Heart disease continues to be a global health issue, causing much morbidity and mortality in spite of the advancement in medical science. Appropriate and timely prediction is crucial for successful clinical intervention, but conventional diagnostic measures are deficient in efficiency and accuracy to enable early-stage detection. Over the last few years, machine learning (ML) has been a high-powered tool used to predict heart disease by analyzing intricate patterns across various healthcare data sets like ECG signals and demographic data. Yet, most current ML-based systems are dependent on centralized repositories of data, which poses legitimate concerns about data privacy, security, and conformance to compliance with regulatory protocols like HIP AA and GDPR. This review looks at the state of heart disease prediction in the context of machine learning and decentralized data exchange technologies. We look at how techniques like federated learning and blockchain are being used in predictive health systems in order to overcome privacy and security constraints of the centralized approaches. The literature reports promising methods that allow collaborative training of models without sharing raw patient data, thus ensuring patient confidentiality while still enjoying high predictive accuracy. In addition, blockchain technology is emphasized to playa crucial role in ensuring data integrity, secure access management, and open auditing.