Wang Dingding, Jiang Jing, Mo Jiaqing, Tang Jun, Lv Xiaoyi
College of Information Science and Engineering, Xinjiang University, Urumqi, China.
The first Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Appl Spectrosc. 2020 Jun;74(6):674-683. doi: 10.1177/0003702820904444. Epub 2020 Apr 1.
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.
本研究旨在利用拉曼光谱结合改进的支持向量机（SVM）来筛查甲状腺功能障碍。在光谱分析中，为了进一步提高支持向量机算法模型的分类准确率，提出了一种基于偏最小二乘法的遗传粒子群优化算法来优化支持向量机（PLS-GAPSO-SVM）。为了评估该算法的性能，使用了五种优化算法：基于网格搜索的支持向量机（Grid-SVM）、基于粒子群优化算法的支持向量机（PSO-SVM）、基于遗传算法的支持向量机（GA-SVM）、基于人工鱼群耦合均匀设计算法的支持向量机（AFUD-SVM）以及基于模拟退火粒子群优化算法的支持向量机（SAPSO-SVM）。在本实验中，对95例确诊甲状腺功能障碍患者的血清样本和90例甲状腺功能正常者的血清样本进行了拉曼光谱检测。实验结果表明，GAPSO-SVM算法具有95.08%的高平均诊断准确率，且具有较高的灵敏度和特异性（91.67%，97.96%）。与传统优化算法相比，该算法具有较高的诊断准确率、较短的执行时间和良好的可靠性。可见，拉曼光谱结合GAPSO-SVM诊断算法在甲状腺功能障碍的无创筛查中具有巨大潜力。
