基于数据挖掘技术的表面增强拉曼光谱诊断肺癌的研究

Study of surface enhanced Raman spectroscopy based on data mining in the diagnosis of lung cancer

目的探讨唾液表面增强拉曼光谱诊断肺癌的可行性,并应用数据挖掘技术得出判别肺癌的较优模型。方法利用便携式表面增强拉曼光谱检测系统对18个健康人和59个肺癌患者的唾液样本进行光谱检测和分析,用数据挖掘技术建立SVM、随机森林模型,与传统的Fisher判别模型进行比较,探讨各个模型对肺癌辅助诊断的性能。结果SVM和随机森林模型的各项诊断指标都高于Fisher判别分析,二者是判别肺癌的较优分类模型。结论研究结果表明,基于数据挖掘技术的唾液表面增强拉曼光谱分析方法可能成为一种新型的肺癌诊断工具。

Objective To investigate the potential feasibility of lung cancer diagnosis with saliva surface-enhanced Raman spectroscopy, and to obtain the relatively optimal diagnosis model of lung cancer by data mining. Methods In this paper, saliva samples of 18 healthy individuals and 59 lung cancer patients were measured and analyzed the spectra by portable SERS detection system. We established the support vector machine （SVM） and random forests by data mining technology, compared with traditional Fisher discriminant model, and then discussed the auxiliary diagnosis efficiency for lung cancer with the models. Results The diagnosis indexes of the SVM and random forest were higher than Fisher discriminant analysis. We considered SVM and random forest were the optimal classification models for the diagnosis of lung cancer. Conclusions The results showed that the study of surface enhanced Raman spectroscopy based on data mining might be a new type tool for the diagnosis of lung cancer.