Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/2D14DD62-E564-4FBF-B5F9-1CE9156BEFC8 | ||
| Year | 1996 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | C. Ohmann, V. Moustakis, Q. Yang, & K. Lang, (1996). "Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain." Artificial Intelligence in Medicine, Vol. 8 Iss. pp. 23‐36. URL:http://www.ncbi.nlm.nih.gov/pubmed/8963379 | ||
| Appears in Collections |
Clinical diagnosis in acute abdominal pain is still a major problem. Computer-aided diagnosis offers some help; however, existing systems still produce high error rates. We therefore tested machine learning techniques in order to improve standard statistical systems. The investigation was based on a prospective clinical database with 1254 cases, 46 diagnostic parameters and 15 diagnoses. Independence Bayes and the automatic rule induction techniques ID3, NewId, PRISM, CN2, C4.5 and ITRULE were trained with 839 cases and separately tested on 415 cases. No major differences in overall accuracy were observed (43-48%), except for NewId, which was below the average. Between the different techniques some similarities were found, but also considerable differences with respect to specific diagnoses. Machine learning techniques did not improve the results of the standard model Independence Bayes. Problem dimensionality, sample size and model complexity are major factors influencing diagnostic accuracy in computer-aided diagnosis of acute abdominal pain.
Copyright © DIAS 2013
