College of Software, Xinjiang University, Urumqi 830046, China.
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Anal Methods. 2021 Oct 14;13(39):4642-4651. doi: 10.1039/d1ay00802a.
The diffuse growth of glioma cells leads to gliomatosis, which has less cure rate and high mortality. As the severity deepens, the treatment difficulty and mortality of glioma patients gradually increase. Therefore, a rapid and non-invasive diagnostic technique is very important for glioma patients. The target of this study is to classify contract subjects and glioma patients by serum mid-infrared spectroscopy combined with an ensemble learning method. The spectra were normalized and smoothed, and principal component analysis (PCA) was utilized for dimensionality reduction. Particle swarm optimization-support vector machine (PSO-SVM), decision tree (DT), logistic regression (LR) as well as random forest (RF) were used as base classifiers, and AdaBoost integrated learning was introduced. AdaBoost-SVM, AdaBoost-LR, AdaBoost-RF and AdaBoost-DT models were established to discriminate glioma patients. The single classification accuracy of the four models for the test set was 87.14%, 90.00%, 92.00% and 90.86%, respectively. For the purpose of further improving the prediction accuracy, the four models were fused at decision level, and the final classification accuracy of the test set reached 94.29%. Experiments show that serum infrared spectroscopy combined with the ensemble learning method algorithm shows wonderful potential in non-invasive, fast and precise identification of glioma patients, and can also be used for reference in intelligent diagnosis of other diseases.
神经胶质瘤细胞的弥漫性生长导致了神经胶质瘤病，其治愈率较低，死亡率较高。随着病情的加重，神经胶质瘤患者的治疗难度和死亡率逐渐增加。因此，对于神经胶质瘤患者来说，快速、无创的诊断技术非常重要。本研究的目的是通过血清中红外光谱结合集成学习方法对合同对象和神经胶质瘤患者进行分类。对光谱进行归一化和平滑处理，利用主成分分析（PCA）进行降维。采用粒子群优化支持向量机（PSO-SVM）、决策树（DT）、逻辑回归（LR）和随机森林（RF）作为基本分类器，并引入自适应增强（AdaBoost）集成学习。建立了 AdaBoost-SVM、AdaBoost-LR、AdaBoost-RF 和 AdaBoost-DT 模型来区分神经胶质瘤患者。四个模型对测试集的单一分类准确率分别为 87.14%、90.00%、92.00%和 90.86%。为了进一步提高预测精度，在决策层融合了四个模型，测试集的最终分类准确率达到 94.29%。实验表明，血清红外光谱结合集成学习方法算法在神经胶质瘤患者的无创、快速、精确识别方面具有巨大潜力，也可作为其他疾病智能诊断的参考。
