Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03067-3_2 | ICDM |
Keywords | Field | DocType |
machine learning algorithms,pilot study,classification rate,electronic nose,electronic nose ovarian carcinoma,ovarian carcinoma,data processing,pilot test,experimental analysis,real ovarian carcinoma sample,multi-sensor data,electronic nose system,machine learning,medicine | Electronic nose,Early detection,Data processing,Computer science,Algorithm,Nose,Software,Artificial intelligence,Classification rate,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.52 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Chilo | 1 | 50 | 5.57 |
| György Horvath | 2 | 2 | 0.52 |
| Thomas Lindblad | 3 | 63 | 9.02 |
| Roland Olsson | 4 | 87 | 6.06 |