BLOOD GROUP ESTIMATION FROM FINGERPRINT PATTERNS

Blood group identification plays a vital role in healthcare, particularly in blood transfusions, organ transplantation, emergency medical services, and surgical procedures. Conventional blood group determination relies on blood sample collection and laboratory-based serological testing, which is accurate but invasive, time-consuming, and dependent on specialized equipment and trained personnel. Recent advancements in biometric technologies, image processing, and machine learning have encouraged the exploration of non-invasive approaches for preliminary blood group estimation. This paper presents a conceptual framework for blood group estimation using fingerprint biometrics, image processing, and machine learning techniques. The proposed framework involves fingerprint image acquisition, image preprocessing, feature extraction, and machine learning-based classification to estimate the most probable ABO and Rh blood group. Fingerprint characteristics such as ridge patterns, minutiae points, ridge density, loops, whorls, and arches are extracted and analysed to identify potential statistical correlations with blood group categories. The prediction model is trained using a labelled fingerprint dataset containing verified blood group information, enabling the system to estimate blood groups based on learned patterns. The proposed framework is intended as a rapid, cost-effective, and non-invasive decision-support tool for preliminary blood group estimation and is not intended to replace conventional laboratory-based blood group testing. The integration of fingerprint biometrics, image processing, and artificial intelligence demonstrates the potential for developing intelligent healthcare support systems that may assist healthcare camps, rural medical centres, and emergency situations where rapid preliminary assessment is beneficial. This study contributes to ongoing research in AI-assisted, non-invasive healthcare applications.