The main goal of the project is to build a blockchain-powered application that ensures secure storage and controlled sharing of medical records, protecting sensitive patient data while making it easily accessible to authorized parties. Blockchain offers immutability, transparency, and decentralization, which are essential in safeguarding medical data against unauthorized access and breaches. The project also integrates machine learning models to extract predictive insights and detect anomalies in medical data, enabling more proactive and personalized healthcare. By the end of the project, students are expected to deliver a functional prototype demonstrating how blockchain can enforce secure, permission-based access, and how ML can support smarter healthcare decisions.
Over a twelve-week period, students will undertake a variety of development and research tasks. The project will begin with an introduction to blockchain and smart contracts, particularly using platforms such as Ethereum, Hyperledger Fabric, or Polygon. Students will explore Solidity for contract development, and learn how to manage and encrypt patient data securely. Parallelly, they will design a simple machine learning module to analyze health records for prediction or anomaly detection using libraries like TensorFlow, PyTorch, or scikit-learn.
Subsequent weeks involve building smart contracts for data access and sharing, creating a decentralized application (DApp) interface for users, and testing functionalities such as record upload, permission granting, and ML-based analysis. The students will also evaluate the system’s performance and security, enhance the interface for usability, and finalize the complete application. The final stages involve documenting the project’s design, implementation, and findings, followed by a team presentation. Throughout the project, students are expected to follow ethical coding practices, ensure data protection principles are respected, and avoid plagiarism.
