Fingerprint-Based Biomarker Recognition for Non-Invasive Blood Group Identification

In emergency medical situations, the rapid and accurate identification of an individual's blood group can be lifesaving. Traditional methods for blood group testing, although reliable, require physical samples, laboratory processing, and time. This research investigates a novel, non-invasive approach to blood group classification using fingerprint patterns. The hypothesis is based on a potential correlation between dermatoglyphic features such as ridges and minutiae and blood group types. In this study, fingerprint images are captured and processed through image preprocessing techniques, followed by feature extraction using Convolutional Neural Networks (CNNs). A supervised learning classifier is then trained to categorize each fingerprint into its corresponding blood group (A, B, AB, or O). The proposed model demonstrates promising accuracy, indicating that biometric traits like fingerprints can be effectively utilized for blood group prediction. This approach holds significant potential to transform healthcare diagnostics by enabling faster, contactless blood group identification especially valuable in rural settings, accident scenarios, and emergency medical camps