Robust and Efficient Approach to Diagnose Sickle Cell Anemia in Blood (original) (raw)
Advances in Intelligent Systems and Computing
AI-generated Abstract
A robust and efficient methodology for diagnosing sickle cell anemia is crucial for timely and effective patient management. This research presents a novel approach that integrates advanced diagnostic techniques with machine learning algorithms to enhance diagnostic accuracy and reduce processing time. The study evaluates the performance of the proposed method against traditional diagnostic practices, demonstrating significant improvements in both sensitivity and specificity. Additionally, the approach offers scalability and cost-effectiveness, making it suitable for widespread clinical application.
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