Detection and recognition of urinary VOCs marker gases for bladder cancer based on electronic nose technology
10.3760/cma.j.cn121382-20240127-00203
- VernacularTitle:基于电子鼻技术的膀胱癌尿液VOCs标志物气体检测与识别
- Author:
Zhijian HUANG
1
;
Yutong HAN
;
Yufan SUN
;
Zhigang ZHU
Author Information
1. 上海理工大学健康科学与工程学院，上海 200093
- Keywords:
Electronic nose;
Bladder cancer;
Volatile organic compounds;
Feature engineering;
Gas recognition
- From:
International Journal of Biomedical Engineering
2024;47(2):115-122
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To design an electronic nose that can detect and identify urinary volatile organic compounds (VOCs) as marker gases for bladder cancer.Methods:Isopropyl alcohol, ethylbenzene, acetic acid, and ammonia were selected as target gases, and 8 metal oxide gas sensors were used to construct sensor arrays for testing and collecting experimental data, and different characteristics were normalized. Recursive feature elimination (RFE) was used to select the best feature subset, and principal component analysis (PCA) and linear discriminant analysis (LDA) were further introduced to reduce the data dimension and facilitate visual analysis. In addition, three machine learning algorithms, including support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), were combined to train and verify the model.Results:When the feature number was 12, the accuracy of the model classification had the best performance. The feature subset consisted of 5 differences, 5 sensitivities, and 2 integrals, and the data was reduced to 12 dimensions. Only PCA couldn’t distinguish the four gases. The LDA classification performance was significantly better than that of PCA, except that isopropyl alcohol and acetic acid had a small overlap area. LDA could distinguish ethylbenzene and ammonia from isopropyl alcohol and acetic acid; the sample points were gathered, which means the clustering performance was also better. The prediction accuracy of SVM, RF, and KNN was 0.85, 0.56, and 0.79, respectively. After model verification, the classification accuracy of PCA+SVM, LDA+RF, and LDA+KNN was 0.97, 0.94, and 0.97, respectively.Conclusions:An electronic nose was designed to detect and identify urinary VOCs marker gases for bladder cancer.
