Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients

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 articial 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.