A decision tree network with semi-supervised entropy learning strategy for spectroscopy aided detection of blood hemoglobin

Abstract

Non-invasive techniques for rapid blood testing are gaining traction in global healthcare as they optimize medical screening, diagnosis and clinical decisions. Fourier transform infrared (FT-IR) spectroscopy is one of the most common technologies that can be used for non-destructive aided medical detection. Typically, after acquiring the Fourier transform infrared spectrum, spectral data preprocessing and feature extraction and quantitative analysis of several indicators of blood samples can be accomplished, in combination with chemometric method studies. At present, blood hemoglobin (HGB) concentration is one of the most valuable information for the clinical diagnosis of patient’s health status. FT-IR spectroscopy is employed as a green technique aided medical test of blood HGB. Then the acquired HGB concentration data is switched to the spectral feature data by the studies of advanced chemometric method, in help for hiding the sensitive medical information to protect the privacy of patients. The decision tree network architecture is proposed for feature extraction of FT-IR data in order to find the small set of wavenumbers that are able to quantify HGB. A semi-supervised learning strategy is designed for tuning the number of network neuron nodes, in the way of searching for the maximum entropy increment. Each neuron is optimized by the growing of a semi-supervised decision tree, to accurately identify the informative FT-IR wavenumbers. The features extracted by the semi-supervised learning decision tree network guarantees the FT-IR aided detection model has high efficiency and high prediction accuracy. A model of quantifying the HGB concentration shows that the proposed decision tree network with semi-supervised entropy learning strategy outperforms the usual methods of full spectrum partial least square model and the fully connected neural network model in prediction accuracy. The framework is expected to support the FT-IR spectral technology for aided detection of medical and clinical data.