Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas,Analytical Methods

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Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas
Analytical Methods ( IF 2.7 ) Pub Date : 2021-08-26 , DOI: 10.1039/d1ay00802a
Hanwen Qu ₁ , Wei Wu ₁ , Chen Chen ₂ , Ziwei Yan ₁ , Wenjia Guo ₃ , Chunzhi Meng ₂ , Xiaoyi Lv _{1,

2,

4} , Fangfang Chen ₂ , Cheng Chen ₁

Affiliation

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.

中文翻译：

血清中红外光谱结合集成学习方法在胶质瘤快速诊断中的应用

胶质瘤细胞的弥漫性生长导致胶质瘤病，治愈率低，死亡率高。随着病情加重，胶质瘤患者的治疗难度和死亡率逐渐增加。因此，快速且非侵入性的诊断技术对于神经胶质瘤患者非常重要。本研究的目标是通过血清中红外光谱结合集成学习方法对合同对象和胶质瘤患者进行分类。对光谱进行归一化和平滑处理，并利用主成分分析 (PCA) 进行降维。使用粒子群优化-支持向量机（PSO-SVM）、决策树（DT）、逻辑回归（LR）和随机森林（RF）作为基分类器，并引入了AdaBoost集成学习。AdaBoost-SVM、AdaBoost-LR、建立 AdaBoost-RF 和 AdaBoost-DT 模型来区分胶质瘤患者。四种模型对测试集的单分类准确率分别为87.14%、90.00%、92.00%和90.86%。为进一步提高预测精度，将四种模型在决策层融合，测试集最终分类精度达到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、精准识别方面显示出惊人的潜力，也可用于其他疾病的智能诊断参考。为进一步提高预测精度，将四种模型在决策层融合，测试集最终分类精度达到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、精准识别方面显示出惊人的潜力，也可用于其他疾病的智能诊断参考。为进一步提高预测精度，将四种模型在决策层融合，测试集最终分类精度达到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、精准识别方面显示出惊人的潜力，也可用于其他疾病的智能诊断参考。

更新日期：2021-09-22

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