Revolutionizing Patient Care: Decentralized Medical Records and ML Based Disease Prediction Platform

In today’s healthcare landscape, obtaining timely online medical consultations and disease predictions poses a significant challenge, often resulting in delayed diagnoses and treatments. To overcome this hurdle, our paper combines Artificial Intelligence & Machine Learning (AI & ML) with Blockchain technology to revolutionize healthcare accessibility and data security. Our paper aims to develop a cutting-edge Decentralized Medical Records, Disease Prediction, and Feedback Sharing Platform on the Blockchain, integrated with IPFS. Addressing the critical need for secure and efficient patient data management in healthcare, the platform utilizes Pinata IPFS Pinning Service for Storage. Featuring a responsive frontend built with React JS and powered by smart contracts written in Solidity, the platform ensures secure data sharing and transparency. Feedback from doctors is securely stored, allowing patients to respond. Machine learning models including Random Forest, Naive Bayes, and Decision Tree algorithms predict diseases, with results stored on the blockchain via Flask integration. Backend development utilizes Node.js, Python, and Hardhat for compilation and deployment of smart contracts. Smart contracts govern access permissions and ownership of medical records, disease predictions and feedback’s providing a secure way to share data among authorized accounts. This comprehensive approach aims to revolutionize medical data management, prediction, and patient feedback, ultimately enhancing healthcare outcomes.