Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm

Abstract

Chronic renal failure (CRF) is a clinically serious kidney disease. If the patient is not treated in a timely manner, CRF will develop into uremia. However, current diagnostic methods, such as routine blood examinations and medical imaging, have low sensitivity. Therefore, it is important to explore new and effective diagnostic methods for CRF, such as serum spectroscopy. This study proposes a cost-effective and reliable method for detecting CRF based on Fourier transform infrared (FT-IR) spectroscopy and a support vector machine (SVM) algorithm. We measured and analyzed the FT-IR spectra of serum from 44 patients with CRF and 54 individuals with normal renal function. The partial least squares (PLS) algorithm was applied to reduce the dimensionality of the high-dimensional spectral data. The samples were input into the SVM after division by the Kennard-Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. The sensitivity of our diagnostic model was 93.75%, the specificity was 100%, and the accuracy was 96.97%. The results demonstrate that FT-IR spectroscopy combined with a pattern recognition algorithm has great potential in screening patients with CRF.