Decentralized and Secure Collaborative Framework for Personalized Diabetes Prediction.

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Blockchain Technology

Background:

• Diabetes poses a global health challenge, necessitating improved prediction models.
• Centralized prediction models face limitations in data diversity and patient privacy.
• Early and personalized diabetes prediction can enhance patient outcomes.

Purpose of the Study:

• To develop a novel framework for diabetes prediction integrating blockchain and federated learning.
• To address privacy risks and data diversity limitations of traditional models.
• To enhance the security and ethical use of healthcare data for diabetes prediction.

Main Methods:

• Utilized blockchain for secure, decentralized data management and access control.
• Implemented federated learning for distributed model training without data sharing.
• Integrated blockchain and federated learning to create a novel collaborative framework.

Main Results:

• The proposed framework demonstrated good predictive performance for diabetes.
• Significant enhancements in privacy and security were achieved compared to centralized methods.
• The framework facilitates the development of robust and personalized diabetes prediction models.

Conclusions:

• The integrated blockchain and federated learning framework offers a promising solution for diabetes prediction.
• This approach enables ethical and effective utilization of distributed healthcare data.
• The framework enhances data security, privacy, and model robustness in medical prediction.