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@article{177564,
        author = {Ayush Darunde and Gaurav Dakhore and Aftab Desai and Gaurang Wankhede and Prof. Dnyaneshwar Kanade},
        title = {Fingerprint Based Blood Group Identification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7528-7533},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177564},
        abstract = {This paper explores the feasibility of using technology to identify a person's blood group with an R2307 fingerprint module integrated with a CP2102 UART-to-USB converter module. The aim is to develop a non-invasive and rapid method for identifying blood groups, particularly useful in emergencies. The system leverages the valley and ridge patterns of fingerprints for analysis and classification. A dataset of fingerprints linked to known blood groups was used for training and validation, with a classification algorithm employed to ensure accurate prediction. Results reveal a significant correlation between fingerprint patterns and blood groups. The proposed system is efficient, cost-effective, and holds potential for integration into healthcare and emergency systems. This research introduces a novel method for blood group identification using biometric technology, with future improvements focusing on machine learning and enhanced accuracy.},
        keywords = {Fingerprint patterns, non-invasive blood typing, CP2102 UART, R2307 fingerprint module},
        month = {May},
        }