Item Infomation
Title: | Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients |
Authors: | Decaro, C. |
Participants: | Montanari, G.B. Molinari, R. Gilberti, A. Bagnoli, D. Bianconi, M. Bellanca, G. |
Issue Date: | 2019 |
Publisher: | IEEE Explore |
Series/Report no.: | IEEE Journal of Translational Engineering in Health and Medicine, 2019, Vol 7, pp 1-8 |
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 arti cial 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. |
URI: | http://tailieuso.tlu.edu.vn/handle/DHTL/10448 |
Source: | http://doi.org/10.1109/JTEHM.2019.2938951 |
Appears in Collections: | Tài liệu hỗ trợ nghiên cứu khoa học |
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