Abstract
In this study, an approach for improving the accuracy of glucose measurement by using handheld devices is presented. The proposed approach is based on reducing the effects of hematocrit. The hematocrit is estimated by using a neural network which is trained by a non-iterative learning algorithm. The inputs for neural network are sampled from the transduced current curve. This current curve is generated during the chemical reactions of glucose measurement process in the handheld devices. The experiments performed on a real dataset show that the accuracy of glucose measurement using the handheld devices is improved by using the proposed approach.
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This work was funded by Industrial University of Ho Chi Minh city (IUH) under grant number 171.2071 (65/HĐ-ĐHCN).
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Huynh, H.T., Quan, H.D., Won, Y. (2017). Accuracy Improvement for Glucose Measurement in Handheld Devices by Using Neural Networks. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2017. Lecture Notes in Computer Science(), vol 10646. Springer, Cham. https://doi.org/10.1007/978-3-319-70004-5_21
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DOI: https://doi.org/10.1007/978-3-319-70004-5_21
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