Abstract
In diagnosis of breast cancer, several different diagnostic tests can be conducted on same patient simultaneously so as to improve the diagnostic results. There are three tumor makers currently available for breast cancer diagnosis, namely CEA, CA15.3, and MCA. The purpose of this study was to investigate the usefulness of neural networks to distinguish breast cancer patients from normal people based on the pattern of the three tumor maker measurements. The neural networks was built and trained with the training data set, and then tested with a separate data set. In order to evaluate the performance of the neural network which differentiated breast carcinoma from normal conditions, an advanced statistical method, relative operating characteristic (ROC) analysis, was utilized.
| Original language | English |
|---|---|
| Title of host publication | Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93) |
| Publisher | Publ by IEEE |
| Pages | 841-844 |
| Number of pages | 4 |
| ISBN (Print) | 0780312333 |
| Publication status | Published - 1 Dec 1993 |
| Externally published | Yes |
| Event | Proceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5) - Beijing, China Duration: 19 Oct 1993 → 21 Oct 1993 |
Conference
| Conference | Proceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5) |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 19/10/93 → 21/10/93 |
ASJC Scopus subject areas
- Engineering(all)
