Survey Paper on Detection of Blood Groups Using Fingerprint Images

Abstract - The detection of blood groups traditionally requires invasive methods, such as blood sample collection and laboratory analysis. This paper presents a novel, non- invasive approach for predicting blood groups by analyzing fingerprint images. Fingerprint patterns are known to be influenced by genetic factors, which also govern blood group types. In this study, a dataset of fingerprint images from in- dividuals with known blood groups (A, B, AB, and O) was analyzed using DL algorithms. Key features were extracted from the finger print patterns, including ridge count, ridge density, and minutiae distribution. These features were used to train a classification model for predicting the blood group. The proposed method achieved a promising accuracy of blood group detection, demonstrating the potential for integrating biometric and genetic information in non-invasive diagnostic tools. This approach could revolutionize medical diagnostics by offering a quick, cost-effective, and non-invasive alternative for determining blood group types. Keywords: Non-invasive blood group detection, Fingerprint analysis, Blood group prediction, Deep learning (DL)