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.

Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients

Decaro, Cristoforo
Primo
Writing – Original Draft Preparation
;
Bellanca, Gaetano
Ultimo
Writing – Review & Editing
2019

Abstract

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.
Decaro, Cristoforo; Montanari, Giovanni Battista; Molinari, Riccardo; Gilberti, Alessio; Bagnoli, Davide; Bianconi, Marco; Bellanca, Gaetano
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11392/2408460
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