Research On Pattern Recognition Of Disease Diagnosis Based On Raman Spectroscopy

Raman spectroscopy is a kind of inelastic scattering spectroscopy.Raman spectroscopybased molecular information detection method has the advantages of high sensitivity,fast detection speed,strong anti-interference ability,etc.,and can realize non-destructive quantitative analysis of samples to be detected.It is a major research hotspot in the field of detection.In this thiesis,two diseases,hydatid disease and chronic renal failure,are selected as the diagnostic objects.The serum Raman spectrum signals of patients with hydatid disease and the urine Raman light signals of patients with chronic renal failure are extracted,and compared with the control group(healthy people)samples.The Raman spectrum signals are compared,and a classification diagnosis model of two diseases is established based on different algorithms to study the pattern recognition and data processing algorithms of Raman spectrum in disease diagnosis.The main research contents of this theiesis are as follows:1.Establish a classification diagnosis model of hydatid disease based on serum Raman spectrum sample data of healthy people and patients with hydatid disease,and use two different algorithms to establish a diagnostic model of hydatid disease,and compare the classification and diagnostic performance of the two models.First,analysis and preprocessing(normalization,subtraction of fluorescence background,baseline correction,etc.)of the original Raman spectrum sample data of serum samples of 173 healthy people and hydatid patients were obtained from the analysis of raw data.The characteristics and similarities and differences of serum Raman spectral signals of patients with worm disease.The raw data was preprocessed to obtain Raman spectral signal data with higher signal-to-noise ratio.Then,a multivariate statistical analysis model(classification diagnosis model)based on the principal component analysis-linear discrimination(PCA-LDA)and partial least squares-linear discrimination(PLSLDA)algorithms was established.Only 69.2308%,and the total prediction accuracy rate of the latter is 92.3077%,and the difference between the two diagnostic accuracy rates is 25%.Therefore,the PLS-LDA classification diagnostic model is more suitable for screening for hydatid disease.2.Urine Raman spectrum signals of healthy people and patients with chronic renal failure(CRF)were combined with pattern recognition algorithms for the diagnosis and screening of CRF diseases.Different pattern recognition algorithms were used to establish a CRF disease classification diagnosis model,and Raman was discussed.Applicability of Spectral Detection in Rapid Screening of CRF Patients.First,the urine Raman spectrum sample data of 92 healthy people and CRF patients were compared and analyzed.Then,the principal component analysis(PCA)was used to extract the features of the sample data to reduce the dimensionality of the high-dimensional spectral data.The classifier algorithm has established back propagation neural network(BP)diagnostic models,genetic algorithm optimized support vector machine(GA-SVM)diagnostic model,grid search optimized support vector machine(GS-SVM)diagnostic model,and particle swarm optimization support.The vector machine(PSO-SVM)diagnostic model has four diagnostic models with accuracy rates of 70.77%,80.77%,84.62%,and 74.62%.Among them,the sensitivity,specificity and accuracy of the best rapid detection model PCA-GS-SVM were 83.33%,85.71%and 84.62%,respectively.The experimental research in this thiesis shows that Raman spectroscopy combined with a multivariate algorithm model can effectively and accurately screen two disease samples.The research in this article is helpful to promote the application research of Raman spectroscopy in the field of infectious disease monitoring,and it has certain reference significance for the application research of Raman spectroscopy in other medical disease diagnosis and medical detection fields.