Raman spectroscopy for rapid and inexpensive diagnosis of echinococcosis using the adaptive iteratively reweighted penalized least squares-Kennard–stone-back propagation neural network

Abstract

A rapid and inexpensive method of screening and diagnosis for echinococcosis is proposed for Raman spectroscopy, together with improved neural networks. We use the adaptive iteratively reweighted penalized least squares (airPLS) algorithm to deduct the fluorescence background from the Raman spectra of healthy people and echinococcosis patients. The processed data was compressed into the principal component by the PLS method, and the Kennard–stone (KS) algorithm was used to divide it into a training set and a testing set. Finally, the data was put into the back propagation (BP) neural network for modeling and prediction. The results show that the true positive rate of echinococcosis diagnosis is (94.2857 ± 4.0721)%, the true negative rate is (95.2381 ± 0)% and the overall accuracy rate is (94.6939 ± 2.3269)%. The algorithm is compared with three other algorithms and it is shown that its superiority can be proved. The Raman spectroscopy combined with the airPLS-KS-BP algorithm can achieve fast and accurate diagnosis of echinococcosis.