Automated Blood Group Classification Using Fingerprint and ML

Blood group identification is a critical component of medical diagnostics, blood transfusion compatibility, and forensic investigations. Traditional blood group detection methods rely on serological testing, which can be time-consuming and require specialized equipment. This study explores an alternative approach that employs fingerprint analysis to predict an individual's blood group. By leveraging biometric features and advanced pattern recognition techniques, this method aims to establish a correlation between fingerprint ridge patterns and blood group classification. The research integrates machine learning algorithms to enhance prediction accuracy, offering a non-invasive and efficient alternative to conventional blood typing methods.