scholarly journals Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 287 ◽  
Author(s):  
Yuichi Sakumura ◽  
Yutaro Koyama ◽  
Hiroaki Tokutake ◽  
Toyoaki Hida ◽  
Kazuo Sato ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Volatile Organic Compounds ◽  
Cancer Patients ◽  
Organic Compounds ◽  
Support Vector ◽  
Exhaled Breath ◽  
Support Vector Machine Algorithm ◽  
Lung Cancer Patients ◽  
Volatile Organic

Volatile organic compounds in exhaled air from patients with lung cancer.

1985 ◽  
Vol 31 (8) ◽  
pp. 1278-1282 ◽  
Author(s):  
S M Gordon ◽  
J P Szidon ◽  
B K Krotoszynski ◽  
R D Gibbons ◽  
H J O'Neill
Keyword(s):  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Cancer Patients ◽  
Organic Compounds ◽  
Gas Chromatography Mass Spectrometry ◽  
Computer Assisted ◽  
Lung Cancer Patients ◽  
Volatile Organic ◽  
Exhaled Air ◽  
Diagnostic Power

Abstract Using a specially developed breath collection technique and computer-assisted gas chromatography/mass spectrometry (GC/MS), we have identified in the exhaled air of lung cancer patients several volatile organic compounds that appear to be associated with the disease. The GC/MS profiles of 12 samples from lung cancer patients and 17 control samples were analyzed by using general computerized statistical procedures to distinguish lung cancer patients from controls. The selected volatile compounds had sufficient diagnostic power in the GC/MS profiles to allow almost complete differentiation between the two groups in a limited patient population.

Quantitative breath analysis of volatile organic compounds of lung cancer patients

Lung Cancer ◽  
2010 ◽  
Vol 67 (2) ◽  
pp. 227-231 ◽  
Author(s):  
Geng Song ◽  
Tao Qin ◽  
Hu Liu ◽  
Guo-Bing Xu ◽  
Yue-Yin Pan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Cancer Patients ◽  
Organic Compounds ◽  
Breath Analysis ◽  
Lung Cancer Patients ◽  
Volatile Organic

scholarly journals Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

Metabolites ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 317
Author(s):  
Michalis Koureas ◽  
Paraskevi Kirgou ◽  
Grigoris Amoutzias ◽  
Christos Hadjichristodoulou ◽  
Konstantinos Gourgoulianis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Ambient Air ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Healthy Controls ◽  
Volatile Organic

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

scholarly journals Microcontroller Implementation of Support Vector Machine for Detecting Blood Glucose Levels Using Breath Volatile Organic Compounds

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2283 ◽  
Author(s):  
Matthew Boubin ◽  
Sudhir Shrestha
Keyword(s):  
Support Vector Machine ◽  
Blood Glucose ◽  
Volatile Organic Compounds ◽  
Embedded System ◽  
Organic Compounds ◽  
Sensor Arrays ◽  
Support Vector ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Volatile Organic

This paper presents an embedded system-based solution for sensor arrays to estimate blood glucose levels from volatile organic compounds (VOCs) in a patient’s breath. Support vector machine (SVM) was trained on a general-purpose computer using an existing SVM library. A training model, optimized to achieve the most accurate results, was implemented in a microcontroller with an ATMega microprocessor. Training and testing was conducted using artificial breath that mimics known VOC footprints of high and low blood glucose levels. The embedded solution was able to correctly categorize the corresponding glucose levels of the artificial breath samples with 97.1% accuracy. The presented results make a significant contribution toward the development of a portable device for detecting blood glucose levels from a patient’s breath.

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