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@article{199261,
        author = {Vidhya. M and Dr.N.Dhivya.,MCA.,M.Phil.,PhD.,},
        title = {Fingerprint-Based Blood Group Identification Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {900-904},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199261},
        abstract = {Blood group identification is a crucial process in medical science, especially during emergencies, surgeries, and blood transfusions. Conventional blood group detection methods involve laboratory testing, which requires blood samples, specialized equipment, and trained medical personnel. These methods are accurate but time-consuming, invasive, and not always accessible in remote or emergency situations. This project, “Fingerprint-Based Blood Group Detection System”, proposes a non-invasive and automated approach for predicting an individual’s blood group using fingerprint images and machine learning techniques. The system is based on the concept that fingerprint patterns are genetically influenced and may have a correlation with blood groups. In this project, a Convolutional Neural Network (CNN) model is trained using a dataset of fingerprint images labeled with corresponding blood groups such as A+, A-, B+, B-, AB+, AB-, O+, and O-. Image preprocessing techniques like resizing and normalization are applied to improve prediction accuracy. The trained model is saved and integrated into a Flask-based web application. The web application allows users to register, log in, upload fingerprint images, and receive instant blood group predictions through a user-friendly interface. The system is developed using Python, TensorFlow, Keras , OpenCV, HTML, and CSS. The experimental results demonstrate that the system is capable of predicting blood groups with reasonable accuracy based on the trained dataset. Although the system is intended for educational and research purposes, it highlights the potential of using artificial intelligence and image processing techniques in healthcare applications. Future improvements include increasing dataset size, enhancing model accuracy, integrating real fingerprint scanners, and deploying the system on cloud platforms for wider accessibility.},
        keywords = {Blood Group Prediction, Fingerprint Biometrics, CNN, Deep Learning, Biomedical Artificial Intelligence.},
        month = {May},
        }