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Tayaqn
AlMarhbi, Araa Abu Rub, Arwa Suroor, Duaa
AlMarhbi, Araa
Abu Rub, Arwa
Suroor, Duaa
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Abstract
Diabetes has become a significant global health concern, demanding innovative
solutions for early detection and effective management. Traditional machine learning
models typically rely on centralized data aggregation, raising substantial privacy issues and compliance risks. This project, Tayaqn: An AI-Driven Diabetes Prediction
System Using Federated Learning Architecture, introduces a novel framework that
employs Federated Learning (FL) to overcome these challenges. FL allows for secure,
decentralized model training, ensuring sensitive health data remains local while enabling collaborative learning across diverse datasets. The system integrates advanced
machine learning and deep learning techniques with robust privacy-preserving mechanisms, achieving high predictive accuracy while safeguarding data security. By
combining scalability, regulatory adherence, and user-friendly design, this project
provides a transformative solution for enhancing early diagnosis and personalized
diabetes management, equipping both patients and clinicians with actionable insights for better healthcare decision-making.