Enhancing Healthcare Analytics with Federated Learning and Cloud Technologies for Improved Patient Outcomes
Keywords:
Federated Learning, Healthcare Analytics, Cloud Computing, Artificial Intelligence, Machine Learning, Data Mining, Privacy-Preserving AI, Deep Learning, Medical Data Security, Patient Outcome OptimizationAbstract
The rapid health system digitization leads to significant accumulation of patient data that sophisticated analytical tools help doctors improve diagnosis accuracy and treatment decisions without sacrificing treatment outcome quality. Traditional centralized systems prevent military-grade machine learning models that handle healthcare analytics from implementation because of privacy regulations and security concerns coupled with regulatory requirements. FL operates as an appealing decentralized structure enabling institutions to develop their models jointly without needing real patient information transfer throughout collaborative training procedures. FL utilizes cloud systems and AI and data mining to develop predictive healthcare analytics which supports patient privacy standards while meeting HIPAA and GDPR requirements in healthcare. This paper studies the healthcare analytics system improvement processes achieved through combining Federated Learning with Cloud Computing and AI-driven Data Mining. This examination describes the cooperation between Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and Transformer-based models to enhance medical picture evaluation and disease manifestation and unique treatment solution forecasting within decentralized networks. SMPC techniques together with differential privacy protocols serve as the central aspect of the study to resolve security and privacy constraints in FL system deployments. The research team will optimize healthcare federation networks through blockchain addition while developing FL architectures and improving network communication systems.
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