International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Blood group detection using fingerprint patterns is an emerging biometric approach that aims to identify a person’s blood group without invasive procedures. Conventional methods require blood sampling, laboratory equipment, and skilled technicians, which may be time-consuming and uncomfortable. This proposed method focuses on analyzing fingerprint ridge characteristics and applying machine learning or pattern-matching techniques to establish a correlation between fingerprint patterns and specific blood groups. By extracting features such as loops, whorls, and arches, and mapping them to biological datasets, blood groups can be detected efficiently. This approach has the potential to provide fast, cost-effective, portable, and non-invasive blood group identification. It can be highly useful in medical emergencies, blood banks, forensic science, and remote healthcare systems. The technology offers scope for automation and can significantly enhance medical record management and identity verification. With further research and large dataset analysis, this method can become a reliable alternative to traditional blood group testing.

Keywords: Blood Group Detection, Fingerprint Recognition, Biometrics, Machine Learning, Non-Invasive Testing, Pattern Analysis, Medical Identification, Ridge Characteristics, Healthcare Technology, Forensics.

Downloads: | DOI: 10.17148/IJARCCE.2025.1411129