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Open AccessArticle

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

1
Department of Information Science and Technology, Aichi Prefectural University, Nagakute 480-1198, Japan
2
Department of Thoracic Oncology, Aichi Cancer Center, 1-1 Kanokoden, Chikusa-ku, Nagoya 464-8681, Japan
3
Department of Mechanical Engineering, Aichi Institute of Technology, Toyota, 470-0392, Japan
4
Department of Materials and Chemistry, National Institute of Advanced Industrial Science and Technology (AIST), Shimo-Shidami, Moriyama-ku, Nagoya 463-8560, Japan
*
Authors to whom correspondence should be addressed.
Academic Editor: W. Rudolf Seitz
Sensors 2017, 17(2), 287; https://doi.org/10.3390/s17020287
Received: 15 November 2016 / Revised: 20 January 2017 / Accepted: 29 January 2017 / Published: 4 February 2017
(This article belongs to the Special Issue Gas Sensors for Health Care and Medical Applications)
Monitoring exhaled breath is a very attractive, noninvasive screening technique for early diagnosis of diseases, especially lung cancer. However, the technique provides insufficient accuracy because the exhaled air has many crucial volatile organic compounds (VOCs) at very low concentrations (ppb level). We analyzed the breath exhaled by lung cancer patients and healthy subjects (controls) using gas chromatography/mass spectrometry (GC/MS), and performed a subsequent statistical analysis to diagnose lung cancer based on the combination of multiple lung cancer-related VOCs. We detected 68 VOCs as marker species using GC/MS analysis. We reduced the number of VOCs and used support vector machine (SVM) algorithm to classify the samples. We observed that a combination of five VOCs (CHN, methanol, CH3CN, isoprene, 1-propanol) is sufficient for 89.0% screening accuracy, and hence, it can be used for the design and development of a desktop GC-sensor analysis system for lung cancer. View Full-Text
Keywords: lung cancer; volatile organic compounds (VOCs); exhaled air; screening; gas chromatography–mass spectrometry analysis; support vector machine (SVM) lung cancer; volatile organic compounds (VOCs); exhaled air; screening; gas chromatography–mass spectrometry analysis; support vector machine (SVM)
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Sakumura, Y.; Koyama, Y.; Tokutake, H.; Hida, T.; Sato, K.; Itoh, T.; Akamatsu, T.; Shin, W. Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm. Sensors 2017, 17, 287.

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