Prediction of blood transfusion donation

Abstract

The goal of the present study was to develop and evaluate machine learning algorithms for the prediction of blood transfusion donation. The machine learning algorithms studied included multilayer perceptrons (MLPs) and support vector machines (SVMs). The methods were evaluated retrospectively in a group of 600 patients and validated prospectively in a group of 148 patients. We reach a sensitivity of 65.8% and a specificity of 78.2% in the prospective group. This discrimination is very interesting because it could allow to propose to the patients, classified as non-donators, to give their blood in the future. Furthermore, the blood transfusion donation UCI corpus used, has been processed in a different manner than the initial marketing one. Therefore, this recent corpus could give a new training set for testing and improving machine learning methods in the future.

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