ijimsrInternational Journal of Innovation in Multidisciplinary Scientific ResearchCoVolume -3 | Issue - 1 | 2025MultidisciplinaryEnglishOCT - DECNNY20251231Engineering and TechnologyFingerprint Based Blood Group Identification Using HOG Feature Extraction with Machine LearningEnglishY924P. SrinivasaRaoEnglishNKuppiliJithiEnglishYKolukuluriSowmyaEnglishYK. SobhaRaniEnglishYhttps://doi.org/10.61239/IJIMSR.2025.3125Identifying blood groups plays crucial role in medical diagnosis, blood transfusions and emergency healthcare. The usual ways to determine blood typification style it rely on serologic tests. To perform these tests, we need medical experts to collect blood samples and to testing blood sample they need reagents, and have access to a laboratory. Usually, they take time, more money and they use the patient!s blood in more quantity. To address this, this study examines a new, non-invasive approach to detect blood groups using fingerprint analysis. Both fingerprints and blood groups come from our genes. So, the patterns in fingerprints like loops, whorls, and arches might link to blood group traits. The new system involves extracting fingerprint features using a Histogram of oriented gradients (HOG), which is a widely used feature descriptor for pattern recognition. These extracted features are then classified using the support vector machine (SVM) and random forest (RF) to predict blood groups. The study evaluates the performance of the model using accuracy, precision, recall, F1 score and confusion matrices. The HOG-SVM achieves high accuracy of 90.69% and HOG-RF model achieves accuracy of 87.36%. andnbsp;In contrast to prior statistical or manual correlation studies, our method applies a fully-automatic machine learning-based pipeline, obtains an accuracy of 90% irrespective of blood samples and laboratory testing. The study results that HOG-SVM performs high classification accuracy compared to HOG-Random Forest.andnbsp;EnglishHistogram of Oriented Gradients (HOG), Fingerprint Feature Extraction, Support Vector Machine (SVM), Blood Group Prediction, Random Forest, Biometric-Based Diagnosishttps://ijimsr.org/admin/abstract?id=42References1619D. S. S. Raja and J. Abinaya, andquot;A Cost-Effective Method for Blood Group Detection Using Fingerprints,andquot; International Journal of Advanced Study and Research Work, vol. 2, no. 3, pp. 1andndash;5, Mar. 2019.P. Palli and S. Mishra, andquot;Inferring Compound Similarity: A Clustering Approach in Drug Discovery,andquot; in Proc. 1st Int. Conf. Cogn. Green Ubiquitous Comput. (IC-CGU), 2024.P. H. Sagar, M. J. Reddy, A. B. Avinash, and V. Jayaprakasan, andquot;Blood Group Detection Using Image Processing MATLAB,andquot; International Journal of Research in Applied Science and Engineering Technology, vol. 9, no. 6, pp. 5006andndash;5010, Jun. 2021.B. 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