A reinforcement federated learning based strategy for urinary disease dataset processing.

Area of Science:

• Medical diagnostics
• Artificial intelligence in healthcare
• Machine learning for disease detection

Background:

• Urinary diseases, including urinary tract infections (UTIs), are increasingly prevalent healthcare challenges.
• Urine tests are crucial for diagnosing conditions like kidney disease and UTIs.
• Current machine learning methods for UTI detection face limitations in data privacy and processing time.

Purpose of the Study:

• To develop a highly accurate UTI detection method that addresses data privacy and processing delays.
• To create a framework leveraging federated learning and reinforcement learning for UTI detection across diverse laboratories.

Main Methods:

• The problem is framed as a combinatorial optimization task, balancing accuracy and computation delay.
• A federated learning and reinforcement learning strategy (FLRLS) is proposed, utilizing lab urine data.
• Deterministic agents optimize data exploration and exploitation within FLRLS, with centralized UTI determination.

Main Results:

• The proposed FLRLS method demonstrates a 5% improvement in diagnostic accuracy.
• Significant reductions in overall processing delay were observed.
• The framework successfully balances accuracy enhancement with minimized computation time.

Conclusions:

• FLRLS offers a robust solution for accurate and efficient UTI detection in healthcare settings.
• The combination of federated learning, reinforcement learning, and combinatorial optimization addresses key limitations of existing methods.
• This approach enhances diagnostic capabilities while ensuring data security and reducing healthcare operational delays.