Abstract

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.

© 2020 The Author(s)

Label-free detection of nasopharyngeal and liver cancer using surface-enhanced Raman spectroscopy and partial lease squares combined with support vector machine

Yun Yu, Yating Lin, Chaoxian Xu, Kecan Lin, Qing Ye, Xiaoyan Wang, Shusen Xie, Rong Chen, and Juqiang Lin
Biomed. Opt. Express 9(12) 6053-6066 (2018)

Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine

Xiangxiang Zheng, Guohua Wu, Jing Wang, Longfei Yin, and Xiaoyi Lv
Biomed. Opt. Express 13(4) 1912-1923 (2022)

Detection of captan residues in apple juice using fluorescence spectroscopy combined with a genetic algorithm and support vector machines

Rendong Ji, Zhezhen Jiang, Xiaoyan Wang, Yue Han, Haiyi Bian, Yudong Yang, Liyun Zhuang, and Yulin Zhang
Appl. Opt. 61(12) 3455-3462 (2022)

Analysis of pesticide residues by a support vector machine combined with fluorescence spectroscopy

Rendong Ji, Yue Han, Xiaoyan Wang, Haiyi Bian, Jiangyu Xu, Zhezhen Jiang, and Xiaotao Feng
Appl. Opt. 60(33) 10383-10389 (2021)

Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM)

Saranjam Khan, Rahat Ullah, Asifullah Khan, Noorul Wahab, Muhammad Bilal, and Mushtaq Ahmed
Biomed. Opt. Express 7(6) 2249-2256 (2016)