Discrimination of nasopharyngeal carcinoma serum using laser-induced breakdown spectroscopy combined with an extreme learning machine and random forest method
| Autor: | Honglin Jin, Shisong Tang, Xiaoyan Zeng, Feng Chen, Yanwu Chu, Yun Tang, Tong Chen, Yongfeng Lu, Lianbo Guo |
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| Rok vydání: | 2018 |
| Předmět: |
Materials science
010401 analytical chemistry medicine.disease 01 natural sciences 0104 chemical sciences Analytical Chemistry Random forest 010309 optics Nuclear magnetic resonance Nasopharyngeal carcinoma 0103 physical sciences Healthy control otorhinolaryngologic diseases medicine Laser-induced breakdown spectroscopy Spectroscopy Extreme learning machine |
| Zdroj: | Journal of Analytical Atomic Spectrometry. 33:2083-2088 |
| ISSN: | 1364-5544 0267-9477 |
| DOI: | 10.1039/c8ja00263k |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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