A NOVEL APPROACH TO PREDICT THE BLOOD GROUP USING FINGERPRINT MAP READING

Abstract

Blood group determination is one of the most critical processes in healthcare, forensic science, and emergency medical situations such as blood transfusions and organ transplants. Traditional methods for determining blood groups rely on invasive techniques such as serological testing, which requires blood samples and chemical reagents, or DNA-based methods that require specialized laboratory equipment. These conventional techniques, while accurate, are time-consuming, expensive, and require trained personnel, making them less feasible for rapid or large-scale applications. This research proposes a novel, non-invasive method to predict blood groups using fingerprint map reading, leveraging the unique ridge patterns, minutiae points, and orientations present in human fingerprints. Fingerprints are not only unique to every individual but also remain unchanged throughout life, making them an ideal biometric for personal identification. By applying advanced image processing techniques to capture and enhance fingerprint features and feeding these features into supervised machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), the system can classify blood groups accurately. Initial experimental results demonstrate a promising correlation between fingerprint characteristics and blood groups, suggesting the viability of this approach for real-time, contactless blood group prediction. The method has significant potential for implementation in hospitals, blood banks, forensic labs, and mobile health devices, providing a safe, cost-effective, and time-efficient alternative to conventional blood typing.