Urine Raman spectroscopy for rapid and inexpensive diagnosis of chronic renal failure (CRF) using multiple classification algorithms

Abstract Chronic renal failure (CRF) is a symptom that is caused by kidney damage that deteriorates to the end stage. If not treated in time, CRF will worse into uraemia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine blood and medical image investigations, have poor sensitivity. Therefore, exploring new and efficient diagnostic methods such as urine spectroscopy for CRF is of great significance. In this study, we first explored Raman spectroscopy to classify urine from CRF patients and control subjects with normal renal function. A total of 48 samples from CRF patients and 44 samples from control subjects were accrued. The spectra revealed relatively lower hydroxybutyrate and higher alanine, creatinine and porphyrin in CRF. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP), grid search support vector machine (GS-SVM), genetic algorithms based on support vector machine (GA-SVM), discriminant analysis (DA) and particle swarm optimization support vector machine (PSO-SVM) algorithms were employed to establish discriminant diagnostic models; the diagnostic accuracy of each of the five classifiers was 70.77 %, 84.62 %, 80.77 %, 65.20 % and 74.62 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in rapid screening of CRF urine samples. Based on the limitations of current routine diagnostic methods, urine Raman spectroscopy may be a replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples.