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Objective: This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. Methods: A spectroscopic setup was prepared for acquisition of blood absorbance spectrum and tested in an operational environment. This setup is non invasive and can be applied during dialysis sessions. A support vector machine and an artificial neural network, trained with a dataset of spectra, have been implemented for the prediction of hematocrit and oxygen saturation. Results & Conclusion: Results of different machine learning algorithms are compared, showing that support vector machine is the best technique for the prediction of hematocrit and oxygen saturation.
Publication date: 
16 Sep 2019

Cristoforo Decaro, Giovanni Battista Montanari, Riccardo Molinari, Alessio Gilberti, Davide Bagnoli, Marco Bianconi, Gaetano Bellanca

Biblio References: 
Volume: 7 Pages: 1-8
IEEE journal of translational engineering in health and medicine