Advancements in Blood Group Classification: A Novel Approach Using Machine Learning and RF Sensing Technology

Abstract

Blood group classification is critical for enhancing the safety of blood transfusions, preventing transfusion-related complications, and facilitating emergency medical interventions and organ transplantation. Unlike traditional methods that require blood draws and chemical reagents, our approach analyzes the unique electromagnetic signatures of blood samples through radio frequency (RF) sensing at 1.2 GHz. We developed a custom software-defined radio (SDR) platform that captures subtle variations in orthogonal frequency-division multiplexing subcarriers, which are then processed by advanced machine learning algorithms including gradient boosting and random forest. Testing on 5840 samples across eight blood groups demonstrated remarkable 97.8% classification accuracy with results delivered in just 1.5 s—significantly faster than conventional 30–60 min laboratory methods. The system’s innovative integration of RF sensing and machine learning eliminates the need for reagents or physical contact while maintaining high precision, offering particular advantages for emergency situations and resource-limited settings. This work represents a paradigm shift in blood typing technology, combining the portability of SDR hardware with the analytical power of machine learning to create a faster safer alternative to traditional approaches. The demonstrated accuracy and speed suggest strong potential for clinical adoption in transfusion medicine and point-of-care diagnostics.