An Intelligent Machine Learning Framework for Enhanced Blood Group Classification and Transfusion Compatibility Prediction: A Novel Deep Learning Approach
Anant Manish Singh, Krishna Jitendra Jaiswal, Arya Brijesh Tiwari, Shifa Siraj Khan, Sanika Satish Lad · International Journal of Research Publication and Reviews · 2025
Blood transfusion safety remains a critical challenge in healthcare systems worldwide with incorrect blood group identification leading to potentially fatal transfusion reactions.This research presents a novel machine learning framework that integrates deep learning algorithms with automated blood group classification to enhance transfusion compatibility prediction and reduce human error in blood banking operations.Our approach combines Convolutional Neural Networks (CNN) with ensemble learning techniques to achieve superior accuracy in blood group detection from digital microscopy images.The proposed system was validated using the UCI Blood Transfusion Service Center dataset containing 748 donor records from Taiwan, supplemented with additional blood cell imaging datasets.The methodology incorporates feature extraction using Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) algorithms, followed by classification through an optimized CNN architecture.Experimental results demonstrate a classification accuracy of 97.8% for ABO blood group prediction, surpassing existing methods by 3.2%.The system achieves a precision of 96.5%, recall of 97.1% and F1-score of 96.8% across all blood group categories.Comparative analysis with traditional manual methods shows a 40% reduction in processing time and 85% decrease in human error rates.The proposed framework successfully addresses key limitations identified in current literature including limited automation, scalability issues and insufficient real-time processing capabilities.Implementation results indicate significant potential for revolutionizing blood banking operations through improved accuracy, reduced costs and enhanced patient safety in transfusion medicine.