A reinforcement federated learning based strategy for urinary disease dataset processing

Abstract

Urinary disease is a complex healthcare issue that continues to grow in prevalence. Urine tests have proven valuable in identifying conditions such as kidney disease, urinary tract infections, and lower abdominal pain. While machine learning has made significant strides in automating urinary tract infection detection, the accuracy of existing methods is hindered by concerns surrounding data privacy and the time-intensive nature of training and testing with large datasets. Our proposed method aims to address these limitations and achieve highly accurate urinary tract infection detection across various healthcare laboratories, while simultaneously minimizing data security risks and processing delays. To tackle this challenge, we approach the problem as a combinatorial optimization task. We optimize the accuracy objective as a concave function and minimize computation delay as a convex function. Our work introduces a framework enabled by federated learning and reinforcement learning strategy (FLRLS), leveraging lab urine data. FLRLS employs deterministic agents to optimize the exploration and exploitation of urinary data, while the actual determination of urinary tract infections is performed at a centralized, aggregated node. Experimental results demonstrate that our proposed method improves accuracy by 5% and reduces total delay. By combining federated learning, reinforcement learning, and a combinatorial optimization approach, we achieve both high accuracy and minimal delay in urinary tract infection detection.

Keywords: Federated learning; Processing delay; Reinforcement learning; Urinary disease.