Vibrational spectroscopy of urine combined with support vector machine for lupus nephritis diagnosis research

Lupus nephritis (LN) is one of the most common and serious organ manifestations of systemic lupus erythematosus (SLE), with a poor long-term prognosis and a complex diagnostic process, therefore it is important to find a simple, rapid and non-invasive method for the diagnosis of LN. This study investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) and Fourier Transform Infrared (FT-IR) spectroscopy of urine samples to classify healthy volunteers and LN patients. SERS and FT-IR data of urine samples were obtained from 100 LN patients and 100 healthy volunteers. To verify the stability of the classification algorithm, 50 independent experiments were conducted. In each experiment, the dataset was randomly divided and a classification model was established using the support vector machine (SVM) algorithm (linear kernel function). Meanwhile, it was compared with four other common classification algorithms and the results showed that SVM model had the best effect. The average classification accuracy of SERS and FT-IR spectra combined with SVM model for 50 independent experiments reached 96.97 % and 92.77 %, respectively. In addition, the features of SERS and FT-IR were spliced and then combined with SVM model for classification, corresponding to an average classification accuracy of 97.63 %. Subsequently, genetic algorithm was used to perform feature selection on the spliced features, and the 16 selected features were also input into SVM model, with an average classification accuracy of 99.47 % over 50 independent experiments. Therefore, urine vibrational spectroscopy combined with SVM model has great potential in the diagnosis of lupus nephritis. • SERS and FT-IR techniques have great potential in the diagnosis of lupus nephritis. • Urine samples were used to classify lupus nephritis patients and healthy controls. • The classification effect is improved after feature fusion and feature selection of spectra.