| Autor: |
Ykhlef, Amel, Labri, Nedjla Selma, Brahami, Menaouer |
| Zdroj: |
International Journal of Information Technology; 20240101, Issue: Preprints p1-12, 12p |
| Abstrakt: |
Blood transfusion is a medical procedure that involves transfusing blood or one of its components from one or more donors into a patient. Digital technology and machine learning have played a crucial role in the blood field and have provided real prospects for the production and distribution of blood products. In this study, we propose supervised machine learning techniques for the multi-label classification of blood products in patients with hematologic diseases. We used three multi-label approaches from the problem transformation category to create a decision support system for blood products label power set (LP), binary relevance (BR), and classifier chain (CC). Multi-label classification using the problem transformation approach is a flexible approach. In this study, we used data from different hospitals in hematology departments and blood transfusion centers to explore the application of contemporary supervised learning algorithms to blood product prediction. The experiment was performed by calculating the Hamming loss and accuracy to facilitate the classification of blood products. As a result, the prediction model achieved an area under the ROC curve of 99.80%, a Hamming loss of 0.30, and an accuracy of 98.8%. The proposed model has been developed to provide accurate and fast results that can save patients’ lives. |
| Databáze: |
Supplemental Index |
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