This study aimed to screen for thyroid dysfunction using Raman spectroscopy combined with an improved support vector machine (SVM). In spectral analysis, in order to further improve the classification accuracy of the SVM algorithm model, a genetic particle swarm optimization algorithm based on partial least squares is proposed to optimize support vector machine (PLS-GAPSO-SVM). In order to evaluate the performance of the algorithm, five optimization algorithms are used: grid search-based SVM (Grid-SVM), particle swarm optimization algorithm-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), artificial fish coupled uniform design algorithm-based SVM (AFUD-SVM), and simulated annealing particle swarm optimization algorithm-based SVM (SAPSO-SVM). In this experiment, serum samples from 95 patients with confirmed thyroid dysfunction and 90 serum samples from normal thyroid function were used for Raman spectroscopy. The experimental results show that the GAPSO-SVM algorithm has a high average diagnostic accuracy of 95.08% and has high sensitivity and specificity (91.67%, 97.96%). Compared with the traditional optimization algorithm, the algorithm has high diagnostic accuracy, short execution time, and good reliability. It can be seen that Raman spectroscopy combined with GAPSO-SVM diagnostic algorithm has enormous potential in noninvasive screening of thyroid dysfunction.
Chaos Particle Swarm Optimization Algorithm for Optimizing the Parameters of Support Vector Machine
