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This paper proposes a comparative system for classifying Parkinson's disease severity using triaxial IMU sensors and ensemble learning, with LightGBM achieving the best performance at around 97% accuracy across metrics.
MA-RAG is a multi-agent retrieval-augmented generation framework for query-driven summarization of longitudinal Parkinson's disease clinical assessments, achieving substantial improvements in factual precision and reducing hallucination rates compared to baseline methods.