Machine learning approach for prediction of hematic parameters in hemodialysis patients

Cristoforo Decaro, Giovanni Battista Montanari, Riccardo Molinariz, Alessio Gilberti, Davide Bagnoli, Marco Bianconix, Gaetano Bellanca
2019 IEEE Journal of Translational Engineering in Health and Medicine  
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
more » ... 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.
doi:10.1109/jtehm.2019.2938951 pmid:32309060 pmcid:PMC6788674 fatcat:5cpax57ekfbd3hx5tkwhmaq5qe