Blood Group Detection Using Fingerprint

Blood group identification is crucial for safe medical procedures, but traditional techniques are based on invasive blood draws, which are risky and inconvenient. This research presents a new, non-invasive method to identify ABO and Rh blood groups from fingerprints. By taking advantage of the biochemical residues in fingerprint sweat, which include blood group-specific antigens, we integrate Fourier-transform infrared (FTIR) spectroscopy and machine learning to identify blood groups. 200 participants with known blood groups provided spectroscopic analysis fingerprint samples to distinguish spectral biomarkers. Supervised learning algorithms based on support vector machines (SVM) and convolutional neural networks (CNN) were then trained to align spectral patterns against blood group antigens. A total accuracy rate of 94.5% was achieved through the suggested methodology, with the precision rate crossing 92% for ABO group discrimination and 96% for Rh factor classification. Comparative verification established its reliability over conventional serological tests. Environmental contamination and inter-individual variability of sweat were overcome with sophisticated preprocessing and feature selection. This non-invasive method exhibits great promise for rapid, cost-efficient blood group screening in clinical, forensic, and resource-scarce contexts. By fusing biometric and biochemical information, this study opens the way for portable, fingerprint-based diagnostic devices, maximizing patient comfort and operational effectiveness within healthcare systems Keywords— Fingerprint recognition, Blood group identification, Pattern recognition, Image processing ,Machine learning, Artificial intelligence, Healthcare technology, Medical diagnostics, Personalized medicine, Fingerprint analysis, Vein pattern recognition, Dermatoglyphics, Biometric authentication, Health informatics