Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis

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Abstract
Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis.
Funding Information
  • Fundamental Research Funds for the Central University (EIPE16313, 2014JQ8365, NCET-12-0447, 61375055)
  • State Key Laboratory of Electrical Insulation and Power Equipment (EIPE16313, 2014JQ8365, NCET-12-0447, 61375055)
  • Natural Science Foundation of Shaanxi Province (EIPE16313, 2014JQ8365, NCET-12-0447, 61375055)
  • Program for New Century Excellent Talents in University (EIPE16313, 2014JQ8365, NCET-12-0447, 61375055)
  • National Natural Science Foundation of China (EIPE16313, 2014JQ8365, NCET-12-0447, 61375055)

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