Discrimination of nasopharyngeal carcinoma serum using laser-induced breakdown spectroscopy combined with an extreme learning machine and random forest method

Yanwu Chu, Tong Chen, Feng Chen, Yun Tang, Shisong Tang, Honglin Jin, Lianbo Guo, Yong Feng Lu, Xiaoyan Zeng

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The early diagnosis of malignant solid tumours remains a challenge. Here, we propose an efficient way to discriminate between nasopharyngeal carcinoma (NPC) serum and healthy control serum by using laser-induced breakdown spectroscopy (LIBS). Serum was dripped onto a boric acid substrate for LIBS spectrum acquisition. The focus elements (Na, K, Zn, Mg, etc.) were selected for diagnosing NPC using LIBS. With the random forest (RF), characteristic spectral lines were selected based on the variable importance. The spectral lines with variable importance greater than the average were selected. The selected spectral lines are the input of the extreme learning machine (ELM) classifier. Using the RF combined with the ELM classifier, the accuracy rate, sensitivity, and specificity of NPC serum and healthy controls reached 98.330%, 99.0222% and 97.751%, respectively. This demonstrates that LIBS combined with a RF-ELM model can be used to identify NPC with a high rate of accuracy.

Original languageEnglish (US)
Pages (from-to)2083-2088
Number of pages6
JournalJournal of Analytical Atomic Spectrometry
Volume33
Issue number12
DOIs
StatePublished - Dec 2018
Externally publishedYes

ASJC Scopus subject areas

  • Analytical Chemistry
  • Spectroscopy

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