Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas. Issue 39 (21st September 2021)

• Record Type: Journal Article
• Authors: Qu, Hanwen; Wu, Wei; Chen, Chen; Yan, Ziwei; Guo, Wenjia; Meng, Chunzhi; Lv, Xiaoyi; Chen, Fangfang; Chen, Cheng
• Is Part Of: Analytical methods. Volume 13:Issue 39(2021)
• Page Start: 4642
• Page End: 4651
• Publication Date: 2021-09-21
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d1ay00802a
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : Diffuse growth of glioma cells leads to gliomatosis, which has a low cure rate and high mortality. This study aims to find an efficient and accurate diagnostic method for glioma by using infrared spectroscopy combined with ensemble learning model and decision level fusion. Abstract : 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.