Electronic Nose Ovarian Carcinoma Diagnosis Based on Machine Learning Algorithms

Authors:
José Chilo;György Horvath;Thomas Lindblad;Roland Olsson
Affiliations:
Center for RF Measurement Technology, University of Gävle, Gävle, Sweden S-801 76;Department of Oncology, Sahlgrenska University Hosp. Gothenburg, Sweden;Department of Physics, Royal Institute of Technology, Stockholm, Sweden S-106 91;Department of Computer Science, Ostfold University College, Halden, Norway N-1757
Venue:
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Year:
2009

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Abstract

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.