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Electronic nose ovarian carcinoma diagnosis based on machine learning algorithms
KTH, School of Engineering Sciences (SCI), Physics.
2009 (English)In: Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings, Springer, 2009, 13-23 p.Conference paper (Refereed)
Abstract [en]

Ovarian carcinoma is one of the most deadly diseases, especially in the case of late diagnosis. This paper describes the result of a pilot study on an early detection method that could be inexpensive and simple based on data processing and machine learning algorithms in an electronic nose system. Experimental analysis using real ovarian carcinoma samples is presented in this study. The electronic nose used in this pilot test is very much the same as a nose used to detect and identify explosives. However, even if the apparatus used is the same, it is shown that the use of proper algorithms for analysis of the multi-sensor data from the electronic nose yielded surprisingly good results with more than 77% classification rate. These results are suggestive for further extensive experiments and development of the hardware as well as the software.

Place, publisher, year, edition, pages
Springer, 2009. 13-23 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 5633
Keyword [en]
Machine learning algorithms, Medicine, Odor classification, Ovarian carcinoma
National Category
Medical and Health Sciences
URN: urn:nbn:se:kth:diva-152762DOI: 10.1007/978-3-642-03067-3_2ScopusID: 2-s2.0-76249097670ISBN: 3642030661ISBN: 978-364203066-6OAI: diva2:751979
9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009; Leipzig; Germany; 20 July 2009 through 22 July 2009

QC 20141002

Available from: 2014-10-02 Created: 2014-10-01 Last updated: 2014-10-02Bibliographically approved

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Lindblad, Thomas
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