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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.
Highlights
The aim of this paper is to present a machine learning approach for estimation of hematic parameters using spectroscopic techniques
The use of visible spectroscopy for hematic analysis is a promising approach, because absorbance spectra of blood contain a lot of information such as hematocrit, oxygen saturation, and platelets [8] and glucose [9] concentrations, allowing the possibility to significantly increase the amount of parameters to be monitored
Hematocrit and oxygen saturation levels of blood samples were predicted by models based on support vector machine and artificial neural networks techniques
Summary
The aim of this paper is to present a machine learning approach for estimation of hematic parameters using spectroscopic techniques. A common non invasive technique exploits photo-diode arrays for evaluation of blood parameters, but it provides a limited amount of information, such as oxygen saturation and hematocrit. The use of visible spectroscopy for hematic analysis is a promising approach, because absorbance spectra of blood contain a lot of information such as hematocrit, oxygen saturation, and platelets [8] and glucose [9] concentrations, allowing the possibility to significantly increase the amount of parameters to be monitored. Once machine learning models are trained (e.g. fitted on training data), they can be used for predictions during dialysis treatment or surgical operations
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