Blood Product Prediction Using Supervised Machine Learning

Blood transfusion is a medical act that involves transfusing blood or one of its components from one or more donors into a patient. Digital technology and machine learning have acquired a crucial role in the blood field and provide real prospects for the production and distribution of blood products. In this study, we propose a supervised machine learning techniques for multi-label classification of blood products in patients with hematologic diseases. We used three multi-label approaches from the problem transformation category: Label Power Set (LP), Binary Relevance (BR), and Classifier Chain (CC), in order to create a decision support system for blood products. In this study, we used data from different hospitals at haematology departments and blood transfusion centers to explore the application of contemporary supervised learning algorithms for modelling blood products. The experiment was performed by calculating the hamming loss and accuracy to facilitate the classification and accuracy of the blood products. The proposed model has been developed to provide accurate and fast results that can save patient’s lives.